diff --git a/Testemotion.py b/Testemotion.py
new file mode 100644
index 0000000..6f0feaf
--- /dev/null
+++ b/Testemotion.py
@@ -0,0 +1,200 @@
+# import cv2
+# import numpy as np
+# from keras.models import model_from_json
+
+
+# emotion_dict = {0: "Angry", 1: "Disgusted", 2: "Fearful", 3: "Happy", 4: "Neutral", 5: "Sad", 6: "Surprised"}
+
+# # load json and create model
+# json_file = open('emotion_model.json', 'r')
+# loaded_model_json = json_file.read()
+# json_file.close()
+# emotion_model = model_from_json(loaded_model_json)
+
+# # load weights into new model
+# emotion_model.load_weights("emotion_model.h5")
+# print("Loaded model from disk")
+
+# # start the webcam feed
+# #cap = cv2.VideoCapture(0)
+
+# # pass here your video path
+# # you may download one from here : https://www.pexels.com/video/three-girls-laughing-5273028/
+# cap = cv2.VideoCapture(r"C:\Users\Admin\emotiontrain\WIN_20240630_00_58_04_Pro.mp4")
+
+# while True:
+#     # Find haar cascade to draw bounding box around face
+#     ret, frame = cap.read()
+#     frame = cv2.resize(frame, (1280, 720))
+#     if not ret:
+#         break
+#     face_detector = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
+#     gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+
+#     # detect faces available on camera
+#     num_faces = face_detector.detectMultiScale(gray_frame, scaleFactor=1.3, minNeighbors=5)
+
+#     # take each face available on the camera and Preprocess it
+#     for (x, y, w, h) in num_faces:
+#         cv2.rectangle(frame, (x, y-50), (x+w, y+h+10), (0, 255, 0), 4)
+#         roi_gray_frame = gray_frame[y:y + h, x:x + w]
+#         cropped_img = np.expand_dims(np.expand_dims(cv2.resize(roi_gray_frame, (48, 48)), -1), 0)
+
+#         # predict the emotions
+#         emotion_prediction = emotion_model.predict(cropped_img)
+#         maxindex = int(np.argmax(emotion_prediction))
+#         cv2.putText(frame, emotion_dict[maxindex], (x+5, y-20), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2, cv2.LINE_AA)
+
+#     cv2.imshow('Emotion Detection', frame)
+#     if cv2.waitKey(1) & 0xFF == ord('q'):
+#         break
+
+# cap.release()
+# cv2.destroyAllWindows()
+
+
+
+import cv2
+import numpy as np
+from keras.models import model_from_json
+
+emotion_dict = {0: "Angry", 1: "Disgusted", 2: "Fearful", 3: "Happy", 4: "Neutral", 5: "Sad", 6: "Surprised"}
+
+# Load json and create model
+json_file = open('emotion_model.json', 'r')
+loaded_model_json = json_file.read()
+json_file.close()
+emotion_model = model_from_json(loaded_model_json)
+
+# Load weights into new model
+emotion_model.load_weights("final_train.h5")
+print("Loaded model from disk")
+
+# Start the webcam feed
+cap = cv2.VideoCapture(0)
+
+# Pass here your video path
+#cap = cv2.VideoCapture(r"C:\Users\Admin\emotiontrain\WIN_20240630_00_58_04_Pro.mp4")
+
+while True:
+    # Find Haar cascade to draw bounding box around face
+    ret, frame = cap.read()
+    frame = cv2.resize(frame, (1280, 720))
+    if not ret:
+        break
+    face_detector = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
+    gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+
+    # Detect faces available on camera
+    num_faces = face_detector.detectMultiScale(gray_frame, scaleFactor=1.3, minNeighbors=5)
+
+    # Take each face available on the camera and preprocess it
+    for (x, y, w, h) in num_faces:
+        # Adjust the rectangle to make it slightly larger
+        padding = 20
+        cv2.rectangle(frame, (x - padding, y - 40 - padding), (x + w + padding, y + h + 10 + padding), (0, 255, 0), 4)
+        
+        roi_gray_frame = gray_frame[y:y + h, x:x + w]
+        cropped_img = np.expand_dims(np.expand_dims(cv2.resize(roi_gray_frame, (48, 48)), -1), 0)
+
+        # Predict the emotions
+        emotion_prediction = emotion_model.predict(cropped_img)
+        maxindex = int(np.argmax(emotion_prediction))
+        
+        # Adjust the text position to come out of the box
+        cv2.putText(frame, emotion_dict[maxindex], (x + 5, y - 80), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2, cv2.LINE_AA)
+
+    cv2.imshow('Emotion Detection', frame)
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+
+cap.release()
+cv2.destroyAllWindows()
+
+# import cv2
+# import numpy as np
+# from keras.models import model_from_json
+
+# # Load the emotion recognition model
+# emotion_dict = {0: "Angry", 1: "Disgusted", 2: "Fearful", 3: "Happy", 4: "Neutral", 5: "Sad", 6: "Surprised"}
+
+# # Load json and create model
+# json_file = open('emotion_model.json', 'r')
+# loaded_model_json = json_file.read()
+# json_file.close()
+# emotion_model = model_from_json(loaded_model_json)
+
+# # Load weights into new model
+# emotion_model.load_weights("emotion_model.h5")
+# print("Loaded model from disk")
+
+# # Start the webcam feed or video file
+# # cap = cv2.VideoCapture(0)  # For webcam
+# cap = cv2.VideoCapture(r"C:\Users\Admin\emotiontrain\WIN_20240630_00_58_04_Pro.mp4")  # For video file
+
+# while True:
+#     # Read frame from video
+#     ret, frame = cap.read()
+#     if not ret:
+#         break
+#     frame = cv2.resize(frame, (640, 360))  # Resize for display
+
+#     # Convert frame to grayscale for face detection
+#     gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+#     face_detector = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
+
+#     # Detect faces in the frame
+#     num_faces = face_detector.detectMultiScale(gray_frame, scaleFactor=1.3, minNeighbors=5)
+
+#     # Create a copy of the frame for segmentation display
+#     segmented_frame = frame.copy()
+
+#     # Process each detected face
+#     for (x, y, w, h) in num_faces:
+#         padding = 20
+#         # Draw rectangle on the original frame
+#         cv2.rectangle(frame, (x - padding, y - 40 - padding), (x + w + padding, y + h + 10 + padding), (0, 255, 0), 2)
+        
+#         roi_gray_frame = gray_frame[y:y + h, x:x + w]
+#         cropped_img = np.expand_dims(np.expand_dims(cv2.resize(roi_gray_frame, (48, 48)), -1), 0)
+
+#         # Predict emotion
+#         emotion_prediction = emotion_model.predict(cropped_img)
+#         maxindex = int(np.argmax(emotion_prediction))
+#         emotion_label = emotion_dict[maxindex]
+
+#         # Draw emotion label on both frames
+#         cv2.putText(frame, emotion_label, (x + 5, y - 80), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2, cv2.LINE_AA)
+#         cv2.putText(segmented_frame, emotion_label, (x + 5, y - 80), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2, cv2.LINE_AA)
+
+#         # Segment the detected face region
+#         face_region = segmented_frame[y:y+h, x:x+w]
+#         lab_face_region = cv2.cvtColor(face_region, cv2.COLOR_BGR2LAB)
+#         pixel_values = lab_face_region.reshape((-1, 3))
+#         pixel_values = np.float32(pixel_values)
+
+#         # K-means clustering
+#         criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.2)
+#         k = 3  # Number of clusters
+#         _, labels, (centers) = cv2.kmeans(pixel_values, k, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)
+#         centers = np.uint8(centers)
+#         segmented_face_region = centers[labels.flatten()]
+#         segmented_face_region = segmented_face_region.reshape(lab_face_region.shape)
+#         segmented_face_region_bgr = cv2.cvtColor(segmented_face_region, cv2.COLOR_LAB2BGR)
+
+#         # Replace the original face region with the segmented face region
+#         segmented_frame[y:y+h, x:x+w] = segmented_face_region_bgr
+
+#     # Display the results in two separate windows
+#     cv2.imshow('Original Emotion Detection', frame)
+#     cv2.imshow('Segmented Emotion Detection', segmented_frame)
+
+#     # Position the windows next to each other
+#     cv2.moveWindow('Original Emotion Detection', 0, 0)
+#     cv2.moveWindow('Segmented Emotion Detection', 650, 0)  # Adjust the x-coordinate for positioning
+
+#     if cv2.waitKey(1) & 0xFF == ord('q'):
+#         break
+
+# cap.release()
+# cv2.destroyAllWindows()
diff --git a/Training.py b/Training.py
new file mode 100644
index 0000000..54e2eb5
--- /dev/null
+++ b/Training.py
@@ -0,0 +1,69 @@
+
+# import required packages
+import cv2
+from keras.models import Sequential
+from keras.layers import Conv2D, MaxPooling2D, Dense, Dropout, Flatten
+from tensorflow.keras.optimizers import Adam
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+
+# Initialize image data generator with rescaling
+train_data_gen = ImageDataGenerator(rescale=1./255)
+validation_data_gen = ImageDataGenerator(rescale=1./255)
+
+# Preprocess all test images
+train_generator = train_data_gen.flow_from_directory(
+        'train',
+        target_size=(48, 48),
+        batch_size=64,
+        color_mode="grayscale",
+        class_mode='categorical')
+
+# Preprocess all train images
+validation_generator = validation_data_gen.flow_from_directory(
+        'test',
+        target_size=(48, 48),
+        batch_size=64,
+        color_mode="grayscale",
+        class_mode='categorical')
+
+# create model structure
+emotion_model = Sequential()
+
+emotion_model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(48, 48, 1)))
+emotion_model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
+emotion_model.add(MaxPooling2D(pool_size=(2, 2)))
+emotion_model.add(Dropout(0.25))
+
+emotion_model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
+emotion_model.add(MaxPooling2D(pool_size=(2, 2)))
+emotion_model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
+emotion_model.add(MaxPooling2D(pool_size=(2, 2)))
+emotion_model.add(Dropout(0.25))
+
+emotion_model.add(Flatten())
+emotion_model.add(Dense(1024, activation='relu'))
+emotion_model.add(Dropout(0.5))
+emotion_model.add(Dense(7, activation='softmax'))
+
+cv2.ocl.setUseOpenCL(False)
+
+emotion_model.compile(loss='categorical_crossentropy', optimizer=Adam(learning_rate=0.0001, decay=1e-6), metrics=['accuracy'])
+
+# Train the neural network/model
+emotion_model_info = emotion_model.fit(
+    train_generator,
+    steps_per_epoch=28709 // 64,
+    epochs=50,
+    validation_data=validation_generator,
+    validation_steps=7178 // 64
+)
+
+
+# save model structure in jason file
+model_json = emotion_model.to_json()
+with open("emotion_model.json", "w") as json_file:
+    json_file.write(model_json)
+
+# save trained model weight in .h5 file
+emotion_model.save('emotion_model.h5')
+
diff --git a/final_train.ipynb b/final_train.ipynb
new file mode 100644
index 0000000..f1f53a9
--- /dev/null
+++ b/final_train.ipynb
@@ -0,0 +1,532 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3cc72be9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cv2\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Conv2D, MaxPooling2D, Dense, Dropout, Flatten\n",
+    "from tensorflow.keras.optimizers import Adam\n",
+    "from tensorflow.keras.preprocessing.image import ImageDataGenerator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d4928762",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_data_gen = ImageDataGenerator(rescale=1./255)\n",
+    "validation_data_gen = ImageDataGenerator(rescale=1./255)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "4e6d25dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 28709 images belonging to 7 classes.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Preprocess all test images\n",
+    "train_generator = train_data_gen.flow_from_directory(\n",
+    "        'train',\n",
+    "        target_size=(48, 48),\n",
+    "        batch_size=64,\n",
+    "        color_mode=\"grayscale\",\n",
+    "        class_mode='categorical')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "bc6ed7ad",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 7178 images belonging to 7 classes.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Preprocess all train images\n",
+    "validation_generator = validation_data_gen.flow_from_directory(\n",
+    "        'test',\n",
+    "        target_size=(48, 48),\n",
+    "        batch_size=64,\n",
+    "        color_mode=\"grayscale\",\n",
+    "        class_mode='categorical')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "157467cd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\convolutional\\base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# create model structure\n",
+    "emotion_model = Sequential()\n",
+    "\n",
+    "emotion_model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(48, 48, 1)))\n",
+    "emotion_model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))\n",
+    "emotion_model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "emotion_model.add(Dropout(0.25))\n",
+    "\n",
+    "emotion_model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))\n",
+    "emotion_model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "emotion_model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))\n",
+    "emotion_model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "emotion_model.add(Dropout(0.25))\n",
+    "\n",
+    "emotion_model.add(Flatten())\n",
+    "emotion_model.add(Dense(1024, activation='relu'))\n",
+    "emotion_model.add(Dropout(0.5))\n",
+    "emotion_model.add(Dense(7, activation='softmax'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "52e388d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cv2.ocl.setUseOpenCL(False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "6242c1e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\keras\\src\\optimizers\\base_optimizer.py:33: UserWarning: Argument `decay` is no longer supported and will be ignored.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "emotion_model.compile(loss='categorical_crossentropy', optimizer=Adam(learning_rate=0.0001, decay=1e-6), metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "5e9318da",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/50\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\keras\\src\\trainers\\data_adapters\\py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
+      "  self._warn_if_super_not_called()\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m279s\u001b[0m 612ms/step - accuracy: 0.2493 - loss: 1.8264 - val_accuracy: 0.3535 - val_loss: 1.7185\n",
+      "Epoch 2/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140us/step - accuracy: 0.3281 - loss: 1.7058 - val_accuracy: 0.5000 - val_loss: 1.5357\n",
+      "Epoch 3/50\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\ProgramData\\anaconda3\\Lib\\contextlib.py:155: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset.\n",
+      "  self.gen.throw(typ, value, traceback)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m128s\u001b[0m 285ms/step - accuracy: 0.3469 - loss: 1.6662 - val_accuracy: 0.3965 - val_loss: 1.5545\n",
+      "Epoch 4/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105us/step - accuracy: 0.4531 - loss: 1.5522 - val_accuracy: 0.6000 - val_loss: 1.5493\n",
+      "Epoch 5/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m129s\u001b[0m 287ms/step - accuracy: 0.3981 - loss: 1.5596 - val_accuracy: 0.4348 - val_loss: 1.4708\n",
+      "Epoch 6/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105us/step - accuracy: 0.4219 - loss: 1.4835 - val_accuracy: 0.4000 - val_loss: 1.9589\n",
+      "Epoch 7/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m124s\u001b[0m 277ms/step - accuracy: 0.4301 - loss: 1.4802 - val_accuracy: 0.4688 - val_loss: 1.4012\n",
+      "Epoch 8/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 209us/step - accuracy: 0.3438 - loss: 1.6358 - val_accuracy: 0.3000 - val_loss: 1.6251\n",
+      "Epoch 9/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m131s\u001b[0m 292ms/step - accuracy: 0.4609 - loss: 1.4150 - val_accuracy: 0.4870 - val_loss: 1.3558\n",
+      "Epoch 10/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105us/step - accuracy: 0.4219 - loss: 1.4125 - val_accuracy: 0.4000 - val_loss: 1.2579\n",
+      "Epoch 11/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m129s\u001b[0m 288ms/step - accuracy: 0.4862 - loss: 1.3543 - val_accuracy: 0.4890 - val_loss: 1.3322\n",
+      "Epoch 12/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105us/step - accuracy: 0.4531 - loss: 1.3892 - val_accuracy: 0.6000 - val_loss: 1.2068\n",
+      "Epoch 13/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m129s\u001b[0m 288ms/step - accuracy: 0.5045 - loss: 1.3060 - val_accuracy: 0.5119 - val_loss: 1.2849\n",
+      "Epoch 14/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105us/step - accuracy: 0.4375 - loss: 1.3599 - val_accuracy: 0.7000 - val_loss: 1.1402\n",
+      "Epoch 15/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m130s\u001b[0m 289ms/step - accuracy: 0.5139 - loss: 1.2785 - val_accuracy: 0.5240 - val_loss: 1.2502\n",
+      "Epoch 16/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70us/step - accuracy: 0.5469 - loss: 1.2937 - val_accuracy: 0.6000 - val_loss: 1.0163\n",
+      "Epoch 17/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m128s\u001b[0m 285ms/step - accuracy: 0.5298 - loss: 1.2368 - val_accuracy: 0.5347 - val_loss: 1.2202\n",
+      "Epoch 18/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105us/step - accuracy: 0.5781 - loss: 1.2432 - val_accuracy: 0.6000 - val_loss: 1.1356\n",
+      "Epoch 19/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m226s\u001b[0m 504ms/step - accuracy: 0.5466 - loss: 1.2033 - val_accuracy: 0.5423 - val_loss: 1.1994\n",
+      "Epoch 20/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105us/step - accuracy: 0.5469 - loss: 1.2765 - val_accuracy: 0.6000 - val_loss: 1.7803\n",
+      "Epoch 21/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m156s\u001b[0m 349ms/step - accuracy: 0.5639 - loss: 1.1667 - val_accuracy: 0.5435 - val_loss: 1.1885\n",
+      "Epoch 22/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140us/step - accuracy: 0.5156 - loss: 1.2494 - val_accuracy: 0.6000 - val_loss: 1.1181\n",
+      "Epoch 23/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m150s\u001b[0m 335ms/step - accuracy: 0.5756 - loss: 1.1338 - val_accuracy: 0.5626 - val_loss: 1.1620\n",
+      "Epoch 24/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105us/step - accuracy: 0.5938 - loss: 1.0722 - val_accuracy: 0.7000 - val_loss: 1.1075\n",
+      "Epoch 25/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m150s\u001b[0m 335ms/step - accuracy: 0.5815 - loss: 1.1179 - val_accuracy: 0.5612 - val_loss: 1.1521\n",
+      "Epoch 26/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105us/step - accuracy: 0.6250 - loss: 0.9446 - val_accuracy: 0.4000 - val_loss: 1.5148\n",
+      "Epoch 27/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m150s\u001b[0m 334ms/step - accuracy: 0.5972 - loss: 1.0763 - val_accuracy: 0.5660 - val_loss: 1.1368\n",
+      "Epoch 28/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140us/step - accuracy: 0.5156 - loss: 1.1572 - val_accuracy: 0.6000 - val_loss: 0.9533\n",
+      "Epoch 29/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m151s\u001b[0m 337ms/step - accuracy: 0.6046 - loss: 1.0614 - val_accuracy: 0.5682 - val_loss: 1.1330\n",
+      "Epoch 30/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105us/step - accuracy: 0.5469 - loss: 1.1777 - val_accuracy: 0.4000 - val_loss: 1.4259\n",
+      "Epoch 31/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m153s\u001b[0m 340ms/step - accuracy: 0.6141 - loss: 1.0329 - val_accuracy: 0.5788 - val_loss: 1.1185\n",
+      "Epoch 32/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105us/step - accuracy: 0.5781 - loss: 1.2862 - val_accuracy: 0.7000 - val_loss: 0.7841\n",
+      "Epoch 33/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m152s\u001b[0m 338ms/step - accuracy: 0.6198 - loss: 1.0141 - val_accuracy: 0.5809 - val_loss: 1.1193\n",
+      "Epoch 34/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105us/step - accuracy: 0.6250 - loss: 1.0519 - val_accuracy: 0.7000 - val_loss: 1.0675\n",
+      "Epoch 35/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m151s\u001b[0m 337ms/step - accuracy: 0.6283 - loss: 0.9873 - val_accuracy: 0.5859 - val_loss: 1.1094\n",
+      "Epoch 36/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133us/step - accuracy: 0.6250 - loss: 0.8176 - val_accuracy: 0.8000 - val_loss: 0.6879\n",
+      "Epoch 37/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m151s\u001b[0m 336ms/step - accuracy: 0.6465 - loss: 0.9620 - val_accuracy: 0.5953 - val_loss: 1.0881\n",
+      "Epoch 38/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105us/step - accuracy: 0.6875 - loss: 0.9423 - val_accuracy: 0.6000 - val_loss: 0.7955\n",
+      "Epoch 39/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m151s\u001b[0m 337ms/step - accuracy: 0.6520 - loss: 0.9433 - val_accuracy: 0.5964 - val_loss: 1.0865\n",
+      "Epoch 40/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140us/step - accuracy: 0.7500 - loss: 0.8291 - val_accuracy: 0.7000 - val_loss: 0.9446\n",
+      "Epoch 41/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m152s\u001b[0m 339ms/step - accuracy: 0.6657 - loss: 0.9089 - val_accuracy: 0.5999 - val_loss: 1.0775\n",
+      "Epoch 42/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105us/step - accuracy: 0.7031 - loss: 0.7722 - val_accuracy: 0.6000 - val_loss: 0.8422\n",
+      "Epoch 43/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m152s\u001b[0m 339ms/step - accuracy: 0.6746 - loss: 0.8869 - val_accuracy: 0.6046 - val_loss: 1.0745\n",
+      "Epoch 44/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105us/step - accuracy: 0.6406 - loss: 0.9025 - val_accuracy: 0.5000 - val_loss: 1.3010\n",
+      "Epoch 45/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m154s\u001b[0m 344ms/step - accuracy: 0.6822 - loss: 0.8565 - val_accuracy: 0.6051 - val_loss: 1.0774\n",
+      "Epoch 46/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140us/step - accuracy: 0.7031 - loss: 0.9168 - val_accuracy: 0.5000 - val_loss: 1.5085\n",
+      "Epoch 47/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m152s\u001b[0m 338ms/step - accuracy: 0.6908 - loss: 0.8484 - val_accuracy: 0.6063 - val_loss: 1.0797\n",
+      "Epoch 48/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140us/step - accuracy: 0.6875 - loss: 0.9255 - val_accuracy: 0.7000 - val_loss: 0.9303\n",
+      "Epoch 49/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m151s\u001b[0m 337ms/step - accuracy: 0.7011 - loss: 0.8277 - val_accuracy: 0.6013 - val_loss: 1.0813\n",
+      "Epoch 50/50\n",
+      "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140us/step - accuracy: 0.7031 - loss: 0.7076 - val_accuracy: 0.3000 - val_loss: 1.4109\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Train the neural network/model\n",
+    "emotion_model_info = emotion_model.fit(\n",
+    "    train_generator,\n",
+    "    steps_per_epoch=28709 // 64,\n",
+    "    epochs=50,\n",
+    "    validation_data=validation_generator,\n",
+    "    validation_steps=7178 // 64\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "fd3209d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m112/112\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 98ms/step - accuracy: 0.6039 - loss: 1.0631\n",
+      "Validation Loss: 1.0816842317581177\n",
+      "Validation Accuracy: 0.6011439561843872\n"
+     ]
+    }
+   ],
+   "source": [
+    "val_loss, val_accuracy = emotion_model.evaluate(validation_generator, steps=7178 // 64)\n",
+    "print(f'Validation Loss: {val_loss}')\n",
+    "print(f'Validation Accuracy: {val_accuracy}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "60a146e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m113/113\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 94ms/step - accuracy: 0.5976 - loss: 1.0949\n",
+      "Test Accuracy: 60.13%\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay\n",
+    "\n",
+    "# Evaluate the model\n",
+    "test_loss, test_accuracy = emotion_model.evaluate(validation_generator)\n",
+    "print(f\"Test Accuracy: {test_accuracy * 100:.2f}%\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "0b9e5447",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m113/113\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 97ms/step\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "       angry       0.14      0.14      0.14       958\n",
+      "     disgust       0.01      0.01      0.01       111\n",
+      "        fear       0.13      0.09      0.11      1024\n",
+      "       happy       0.25      0.28      0.27      1774\n",
+      "     neutral       0.18      0.16      0.17      1233\n",
+      "         sad       0.16      0.19      0.18      1247\n",
+      "    surprise       0.12      0.11      0.11       831\n",
+      "\n",
+      "    accuracy                           0.18      7178\n",
+      "   macro avg       0.14      0.14      0.14      7178\n",
+      "weighted avg       0.17      0.18      0.17      7178\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Generate predictions\n",
+    "Y_pred = emotion_model.predict(validation_generator)\n",
+    "y_pred = np.argmax(Y_pred, axis=1)\n",
+    "\n",
+    "# Get true labels\n",
+    "y_true = validation_generator.classes\n",
+    "\n",
+    "# Classification report\n",
+    "class_labels = list(validation_generator.class_indices.keys())\n",
+    "report = classification_report(y_true, y_pred, target_names=class_labels, output_dict=True)\n",
+    "print(classification_report(y_true, y_pred, target_names=class_labels))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "20d63e57",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Precision, Recall, F1 Score\n",
+    "precision = report['weighted avg']['precision']\n",
+    "recall = report['weighted avg']['recall']\n",
+    "f1_score = report['weighted avg']['f1-score']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "094c96a0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Precision: 0.17\n",
+      "Recall: 0.18\n",
+      "F1 Score: 0.17\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Precision: {precision:.2f}\")\n",
+    "print(f\"Recall: {recall:.2f}\")\n",
+    "print(f\"F1 Score: {f1_score:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "76643b3c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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H+f6jodQM8uLMxRgmz/oDpbsjA7o3M3R4j1RWdi6Tpi0hwM+dF/s/ZehwAOhXV4m3oyXz7hgkeyL21t92fIaa6LRc3u8UTG1PO07HZVBHaUc1Nxu+2nGlnCP+b1re1nVXxdeDoEAvxr4xj2MnLtOsSU0cHGx549V+LFy8gY2bD6FQKGjVvA6BAUpMjLC53dTUlFEv92bZko1MnDAPExMFNWr6Uzsk8IHOVxjJB97UN/ozbe4f9HhxBqYmJtSo6k3nNvW4cKWoK/H3dfs4czGaL/83BKW7EyfORhSNUXF2oKmRjsO7K5meXHGFh4fz/vvvc+DAAZKSkigsLOoHj4qKonbtou6AunXr6sp7eXkBkJiYSM2aNQkLC6Nv3756dTZt2rRYouLv76+XpACMGjWKJk2acO3aNXx8fPj5558ZNmzYXV/EM2bM4MMPP3y4Cy4lKysLvJWueCtdqRXsx4jxc/h3+zGe66OfkOw5cA61Oo/2beqXa3ylNXXeGl4f0pF+nRoBULuaN9HxKcxevOmxTlSyc9S89fFirK0t+OTtwXoDOQ2lb6iSOkp7vt4TSXpu/j3LZqjzSc3W4G5bNEgz2M0WV1sLPummPyZkWFNfriRn881ew4+BuBdnJ3vc3RyJT0jR7asXWpW5X76KKiMbUxMTbG2teGncLDzcnQwX6D1U8Vfy3vvDyMlRk59fgL29DZ/PWIb/ja5hBwdb8vMLyM7K1WtVycjIJjDI+27VlitfL1e+m/ESObkasrJzcXNxYPLnv+Lt6UyuOo9vl27is3efp/WNsUfBgV5cjIjjl9W7Kl6iUslV6ESlZ8+e+Pn5sWDBAry9vSksLCQkJASN5tZUSXNzc92/byYRNxMarVZbLLHQaovPQLC1Ld5d0qBBA+rVq8eSJUvo3Lkzp0+fZu3atXeN9d1332XChAm6xyqVCj8/vwe80rKh1UJeXvHxAP9uP0azRjVwuku3kLHIydWguGPkuqmJCYV3mVHyOMjKzuXNjxZhbm7Gp+8+j6WF+f1PesT6hSoJ9bLn671XScm+/+ByG3NTnKzNUamLEpqtl5I4cDVNr8ykp6ry15kEzhpgenJpZWRkk5yiwsmp+NRjB3sbAM6ci0ClyqKxkY+HsLYuGjifmJBK1NV4evZuBRQlMqamJpw/H0mjxkUf9OnpmcReS6JPv7YGi7ck1lYWWFtZoMrM4cDxS7w6tCv5BQXk5xcUm3FlYmJCYQnv8UZPun4qpuTkZM6fP8/333/PE088AcCePXtKVUfNmjU5dOiQ3r4jR4488PkjR45k1qxZXLt2jQ4dOtwz8bC0tMTSsuxm0+TkqomNv/WNLiExlfDIOOztrHGws2H56l00a1wDFyd7MjKzWbfpMEkpqmLro8TGJ3PmwlU+entwmcX2qHR+IoRZP2/C19OZmkFenL4Yw7e/bWdQT+NcFyE7R821+GTd47jEVC5FxOJgZ4OnuxOqjGwSktJISin6cI66lgSAi5M9rs72ZOeomfDhInI1Gt4f/yxZ2Wqysou6D50cbDE1Lf+m3KfrKmno68hPB6NR5xdgb1nUupObV0heoRYLUwWda3pwKlaFKjcfFxtzutX2IEtTwOm4ouu8ORPoTqnZeQ+U+JS13FyNXutI4vU0Iq/GY2drjZ2dNStX76JZ45o4OdlxPSmN5St3YG9nQ9Pb1lrZvusEPt5uONjbcOnyNRYt20S3zs3w9nIt9+uBomu6fj1V9zg5KZ3o6ARsba1xcXHg2NEw7OyK/n3t2nVWrdhGvfrVqFW7qPvH2tqSFq1C+XPVDmxtrbG1teLPVTvw9nGjZi1/g1zTnQ4cu4hWq8Xfx53ouGTmLdqAv48bPTs0wszMlIYhgcz7eQOWFuZ4uTtx7GwEG7Yf4/Xh3Q0deulJ10/F5OzsjKurKz/88ANeXl5ERUXxzjvvlKqOcePG0aZNG2bOnEnPnj3Ztm0bGzZseOA+2MGDB/PWW2+xYMEClixZ8l8u4z+7FB7L2x8v0j3+Yem/AHRoU59xI3sQHZvElpknSM/IxsHehupB3nwxdTj+fh569WzafhxXZ3sa1q1anuH/JzPefJZPv1/PpC9WkJSaidLNkaF9W/HWiC6GDq1EYeHXeO2DhbrH83/+B4AuTzZg8rhn2HP4AjPm/6E7PvXGejgv9n+K4QPaExZ+jXOXimaNDBgzU6/uFd+9hZeHM+WtVaALAGNbB+jt/+3YNQ5Hp6PVgpeDJY39/LA2N0WVm8flpGyWHo5BnV98irIxCI+I5aMZy3SPl/y6GYC2resyclhXoqIT2bXnFFnZuTg72VGnVgCvj+2ra40AiItL4beV28nMzMHDzYm+vVrRvYvhuiOjrsYz57b1lf5YuR2AZi3qMGRYN9LTM/lj5XYyVFk4ONrRrHkdunZvoVfHM/2fwtTEhJ8W/I1Gk0+Nmv4MGdbVKNZQAcjMzuWbJf+SmJSOg70NT7aow+gXOuu6RqdNHMjXS/5lyle/o8rMRunuzCvPd6Jf1wrYTVzJW1QU2pL6OiqILVu28Nprr3HlyhVq1KjB3LlzadeuHatXr6Z+/foEBgZy/Phx6tevD0BaWhrOzs5s375dN+h1wYIFfPjhh6SkpNC5c2caN27M/PnziYuLA4qmJ69Zs0ZvJtHthgwZwvr164mNjS1Vi4lKpcLR0ZF1RyKwtTOOlR7LQpMbH2SPk/PXHv3A5/L00zHDTy8ta12qP35/d0k56vsXqmAaez1ev6fMDBWt6viSnp6Og4NDmdd/83PC8qmPUZg93GrU2vxc1Nvef2SxPkoVtkUFoEOHDpw7d05v3+151505mJOTU7F9o0aNYtSoUXqPq1W7NdBq6tSpTJ069a4xxMXFMXjw4DLt1hFCCCF0pOuncvvyyy/p2LEjtra2bNiwgcWLF/PNN9/c97yUlBQ2bdrEtm3bmD9/fjlEKoQQolKq5F0/lT5ROXToEJ9//jkZGRkEBQUxd+5cRo4ced/zGjZsSGpqKp999hk1atS4b3khhBBClF6lT1RWrFjxn86LjIws20CEEEKIEpXFvXqk60cIIYQQj0Il7/qpuCmWEEIIIR570qIihBBCGDOFogxm/VTcFhVJVIQQQghjVsmnJ1fcyIUQQgjx2JMWFSGEEMKYVfLBtJKoCCGEEMasknf9SKIihBBCGLNK3qJScVMsIYQQQjz2pEVFCCGEMGbS9SOEEEIIoyVdP0IIIYQQxklaVIQQQggjplAoUFTiFhVJVIQQQggjVtkTFen6EUIIIYTRkhYVIYQQwpgpbmwPW0cFJYmKEEIIYcQqe9ePJCoGFuLjiIODg6HDKDOmJhX3xXA3Nb3tDR1CmfrQLdjQIZQ5C7PHrxdba+gAHgEL08fr96SyLjR0CJWCJCpCCCGEEZMWFSGEEEIYLUlUhBBCCGG0Knui8nh1GAohhBDisSItKkIIIYQxk+nJQgghhDBW0vUjhBBCCGGkpEVFCCGEMGIKBWXQolI2sRiCJCpCCCGEEVNQBl0/FThTka4fIYQQQhgtaVERQgghjFhlH0wriYoQQghhzCr59GTp+hFCCCGE0ZIWFSGEEMKYlUHXj1a6foQQQgjxKJTFGJWHnzVkOJKoCCGEEEassicqMkZFCCGEEEZLWlSEEEIIYyazfoQQQghhrG52/Tzs9l/NmDEDhULB+PHjdfu0Wi1Tp07F29sba2tr2rVrx9mzZ/XOU6vVjBs3Djc3N2xtbenVqxcxMTGlfn5pUXlMzFuymX92nuTy1USsLM1pHBrI5NE9qebvqSvz5cIN/LXlGLGJaViYmxJaw493XupOwzoBhgu8FPYeu8y8pVs4eSGK+CQVy74YRfd29QwdVqnsO36Zr5dt5WRYNAlJKhZ/NpJubevqjq/bfpLFa/Zy6kI0KelZbFsyidDqvgaM+N6WrtnL0jV7iYlPAaB6oJLXh3bmyea1AJj500bWbjtObGIa5mamhNbwZdKo7jSo7W/IsO9p//HLfPPrNk7d+B39PGMEXW/7HSlbvl7iee+P7cXYwe3LK8xSmbtkM//sOMnlqESsLIreH/43Rv/9QavV8tXCjSz7ex/pqhwa1PFnxpvPUCPIy4CRl05sYhoffv0XW/edI1edR9UqHsyZPIj6taoYOrQK6/Dhw/zwww/UrVtXb//nn3/OzJkzWbRoEdWrV2fatGl07NiRsLAw7O3tARg/fjxr165l+fLluLq68uabb9KjRw+OHj2KqanpA8dQYVpU2rVrp8vmAgICmD17tkHjMTb7T1xmWL8nWPfDGyyfPYaCggIGvvEt2TlqXZkgP3c+mfAM25a8zZpvXsdP6cLAN74lOTXTgJE/uOwcNSHVffh8Yn9Dh/KfZedoqBPsw6dvPlvy8Vw1zeoWfYhUBEp3R955uQfrFkxg3YIJtGwYzMj3FhIWEQcU/c19NL4fmxZN5I+vx+GndOH5N78jOc14/+ayczXUqebD9AnPlHj81NqP9bZZ7w1EoVDQw4iT5v3HL/Pi00+w/oc3+H1O0fvDgPH67w9fL9vK98u388mEZ9iwcAIeLvY8N/4bMrNyDRj5g0tTZdPtpVmYm5ry++zR7Fs+mY9e74ujvbWhQ3tohmpRyczMZPDgwSxYsABnZ2fdfq1Wy+zZs5k8eTL9+vUjJCSExYsXk52dza+//gpAeno6Cxcu5KuvvqJDhw40aNCAZcuWcfr0abZs2VKqOCpki8rhw4extbU1dBgAREZGEhgYyPHjx6lfv77B4vh15mi9x7PeG0xoj8mcCoumef1qAPTr1FivzNTX+vLbugOcC7/GE41rlFus/1XHVnXo2KqOocN4KB1a1qZDy9p3Pd6/a1MAomKTyyukh9KxVYje40mjurN0zT6On71KjUAv+nRspHf8/Vf7sHz9Qc6Hx9K6UfXyDPWBtW9Rm/Yt7v478nB10Hv87+4ztGpYDX8ft0cd2n/226w73h8mDya0+2ROXoimRYNqaLVaFqzYyetDO+laKee8/zx1e/yPPzcfZUifVoYIu1TmLN2Mj4cT8z94XrevirerASMqO2U560elUuntt7S0xNLSssRzxo4dS/fu3enQoQPTpk3T7Y+IiCA+Pp5OnTrp1dO2bVv27dvHyy+/zNGjR8nLy9Mr4+3tTUhICPv27aNz584PHHuFaVG5nbu7OzY2NoYOw6ipsnIAcHIo+eekyctn2V/7cLCzpnY1n/IMTTymCgoK+XvrMXJy1TQMCSh2XJOXz69/78fBzoraVb3LP8BH4HqKii37zjKoZ3NDh1IqGTfeH5xvvD9ExSaTmKyibdOaujKWFma0qF+VI6cjDBJjaW3cdYb6tarw4rsLqdHlXdq98BlL1uw1dFhGx8/PD0dHR902Y8aMEsstX76cY8eOlXg8Pj4eAE9PT739np6eumPx8fFYWFjotcTcWeZBGWWikpWVxZAhQ7Czs8PLy4uvvvpK7/idXT9Tp06lSpUqWFpa4u3tzWuvvaY7FhcXR/fu3bG2tiYwMJBff/1V7/zIyEgUCgUnTpzQnZOWloZCoWDHjh0ApKamMnjwYNzd3bG2tiY4OJiff/4ZgMDAQAAaNGiAQqGgXbt2Zf7zKC2tVsvUuWtoWjeImkH6Hwib956hWoeJBD75Fgt+38Hy2aNxdbIzUKTicXAhPJaand+mWoeJvPfVSn6YNpzqAUrd8S37zlKz89sEd5jEjyt38stXo3F5TP7mfv/nMHY2VnRra7zdPnfSvT/UC6LmjYQxMSUDAHcXe72ybi72JCZnlHuM/8XV2CR+/nMPQX7urJwzhmF9W/HuzD9Y/s9BQ4f20Mqy6yc6Opr09HTd9u677xZ7vujoaF5//XWWLVuGlZXVPeO6nVarvW/Lz4OUuZNRdv1MnDiR7du3s3r1apRKJe+99x5Hjx4tsWtl1apVzJo1i+XLl1OnTh3i4+M5efKk7viQIUNISkpix44dmJubM2HCBBITE0sVz/vvv8+5c+fYsGEDbm5uXL58mZycom8khw4domnTpmzZsoU6depgYWFRYh1qtRq1+lZ/8J3Nb2XpvZmrOB8ey5pviw/6a9UwmM2LJpGSlsUva/fx8vuLWL9gAm7O9iXUJMT9BVXxYOPCt0jPzGHDzlNMmP4rK+a9qktWWjaoxsaFb5GSnsVvaw8wZspi/vp+/GPxN7d83QH6dW6ElaW5oUN5YO99tYpzl2P567vi7w93fn5otRXnpruFhVrq16rC+2N6AVC3hh8XIuL5+Y89DOjWzMDRPaQynJ7s4OCAg4PDPYsePXqUxMREGjW61XVbUFDArl27mD9/PmFhYUBRq4mX163B1omJibpWFqVSiUajITU1Va9VJTExkZYtW5YqdKNrUcnMzGThwoV8+eWXdOzYkdDQUBYvXkxBQUGJ5aOiolAqlXTo0IEqVarQtGlTRo0aBcCFCxfYsmULCxYsoFmzZjRs2JAff/xRl2Q8qKioKBo0aEDjxo0JCAigQ4cO9OxZNNjR3d0dAFdXV5RKJS4uLiXWMWPGDL3mNj8/v1LF8KAmz1zFpj1nWDXvVbw9nIodt7G2JNDXnUYhAcx8dxBmpib8tvbAI4lFVA4W5mYE+LpTr2YV3nm5B7WqefPTyl264zbWlgT4utOwTgBfvDMAU1MTlq+v+N9yD5wI53JUIoN7tjB0KA/s5vvDH/P13x88brSk3Nl6kpyaUayVxVh5ujlQI1Cpt696gCcxCakGiqjiat++PadPn+bEiRO6rXHjxgwePJgTJ04QFBSEUqlk8+bNunM0Gg07d+7UJSGNGjXC3Nxcr0xcXBxnzpyp+IlKeHg4Go2GFi1uvfhdXFyoUaPkwZ7PPvssOTk5BAUFMWrUKFavXk1+fj4AYWFhmJmZ0bBhQ135atWqFeszu5/Ro0ezfPly6tevz6RJk9i3b1+pr+vdd9/Va26Ljo4udR33otVqee+rVWzYeYqVc8c+8CAyrRbUefllGouo3LTaovEodz0OaDQV/2/u13UHqFvTjzrBxj/G6+b7wz87TrFyXvH3hyrerni4OrDrcJhunyYvn/0nwmkcGlje4f4nzeoGcflqgt6+8KhE/JQlf3msSMp71o+9vT0hISF6m62tLa6uroSEhOjWVJk+fTqrV6/mzJkzDBs2DBsbGwYNGgSAo6MjI0aM4M0332Tr1q0cP36c559/ntDQUDp06FCq6ze6rh+tVluq8n5+foSFhbF582a2bNnCmDFj+OKLL9i5c+dd67p9v4mJSbF9eXl5euW7du3K1atXWb9+PVu2bKF9+/aMHTuWL7/88oHjvNfI6rLw3lcrWb35GD9/OhI7GysSk4u6luztrLC2tCA7R82cxZvo1DoUTzcHUtKzWPznHuKup9HzyfqPLK6ylJmtJiL6uu7x1dhkTofF4ORoU2HejDKz1UTE3LqGqNhkTl+MwdnBBl+lC6npWcQkpBKflA7A5atF3ZQerg54ut67udYQPvthPe2a1cTbw5ms7Fz+3nacAycus+SLl8nOUTNv6RY6tqqDh6sDqelZLF2zl/jraXR/0njHdGTd+TuKS+bMxRicbvyOADKyclm77QRTx/U2VJil8u6XN94fPiv5/UGhUDCqf1vmLtlMoJ8bQb7uzF2yGWsrc/rdMXPLWL0y8Em6jpzJzEX/0qd9Q46du8qSNfuY+e4AQ4f20IzxXj+TJk0iJyeHMWPGkJqaSrNmzdi0aZNuDRWAWbNmYWZmRv/+/cnJyaF9+/YsWrSoVGuoACi0pc0MHrHMzExcXFxYtmwZ/fsXrZeRmpqKr68vo0aNYvbs2QQEBDB+/Hi9VfJuCgsLo2bNmhw9ehQbGxtq1arFkSNHdH1tly9fJjg4mFmzZjF+/HhycnKwsbFh/fr1dOvWDYDNmzfTqVMntm/fXuLg2O+//56JEyeiUqmIjY3Fx8dH7zkehEqlwtHRkci4lPv2Fz4I71YlL0I1671BPNe9GbnqPMZOXcLxc1dJSc/E2cGWerWqMH5YJ+rXKrvFt6wtSvcHWBp7jl6k5ytzi+0f2L0Z30x94ZE9b35BYZnVtffoJfqMnVds/3PdmjL/g+f5bd1BXpv2S7HjE0d0YdKobmUSQ46m5G7U/2Lip8vZe+wiickq7G2tqVnVi9GD2tOmSQ1y1Xm89tFSjp+PIjU9EycHW+rVrMJrQzpSr4wX4LIwK7vG4b3HLvH0q/OL7e/frSlz/zcYgKVr9vHBnD85ufZjHOwezTodZfnG7HWXRepmTy56f4BbC74t/Wsf6RnZNKhdtOBbzTKcoWVh+mgb8f/dc4aPv/mbK9HXqeLtypiBTz7SqdUqlQovdyfS09PL5H28pPodHR3xHvkrJhYPN9O1UJNN7I+DHlmsj5LRtajY2dkxYsQIJk6ciKurK56enkyePFnX8nGnRYsWUVBQQLNmzbCxsWHp0qVYW1vj7++Pq6srHTp04KWXXuLbb7/F3NycN998E2tra112aW1tTfPmzfn0008JCAggKSmJ//3vf3rP8cEHH9CoUSPq1KmDWq1m3bp11KpVtPKmh4cH1tbWbNy4EV9fX6ysrHB0dHy0P6QSxO6dc8/jVpbmLJwxopyieTRaN6pO6uHiHyAVSatGwVw/UDzZumlgj2YM7FFxBv598c7dv61aWZrzwyfDyzGastGqYTDx++79enqhT0te6FO6fnZDirvP9UDRN+63RnblrZFdyyGiR6Nz6xA6tw65f0FRoRjdGBWAL774gjZt2tCrVy86dOhA69at79pa4eTkxIIFC2jVqhV169Zl69atrF27FlfXoj7YJUuW4OnpSZs2bejbty+jRo3C3t5eb8rVTz/9RF5eHo0bN+b111/XW9gGwMLCgnfffZe6devSpk0bTE1NWb58OQBmZmbMnTuX77//Hm9vb3r3rhhNwUIIISoIRRltFZTRdf08ajExMfj5+enGmhhKWXf9GItH2fVjKGXZ9WMMyrLrx1iUZdePsXgc35gfdddPeSuvrh+fl34rk66faz8MlK4fY7Rt2zYyMzMJDQ0lLi6OSZMmERAQQJs2bQwdmhBCCCHu47FPVPLy8njvvfe4cuUK9vb2tGzZkl9++QVz84qzQJMQQojKyxhn/ZSnxz5R6dy5c6lufiSEEEIYEwVlkKhU4EEqj1eHoRBCCCEeK499i4oQQghRkUnXjxBCCCGMVxnelLAikq4fIYQQQhgtaVERQgghjJh0/QghhBDCaEmiIoQQQgijpVAUbQ9bR0UlY1SEEEIIYbSkRUUIIYQwYkUtKg/b9VNGwRiAJCpCCCGEMSuDrh+ZniyEEEII8QhIi4oQQghhxGTWjxBCCCGMlsz6EUIIIYQwUtKiIoQQQhgxExMFJiYP1ySifcjzDUkSFSGEEMKISdePEEIIIYSRkhYVAztzLR3b9EJDh1FmmgS6GDqEMnclMcvQIZSpt9edM3QIZe6FZj6GDqHMJWfnGTqEMtfW383QIZSpzIzMcnkemfUjhBBCCKNV2bt+JFERQgghjFhlb1GRMSpCCCGEMFrSoiKEEEIYscreoiKJihBCCGHEKvsYFen6EUIIIYTRkhYVIYQQwogpKIOuHypuk4okKkIIIYQRk64fIYQQQggjJS0qQgghhBGTWT9CCCGEMFrS9SOEEEIIYaSkRUUIIYQwYtL1I4QQQgijVdm7fiRREUIIIYxYZW9RkTEqQgghhDBa0qIihBBCGLMy6PqpwAvTSqIihBBCGDPp+hFCCCGEMFLSolJBnT4fyaq1e7kcEUdKagbvvzmAlk1q6Y4vW7mdnfvPcD05HXMzU6oFejP0ufbUDPbVlUlJy2Dhsk0cP32F7Fw1vl5uPNfnCZ5oXscQl3RfDfpMIToupdj+4U8/weeT+hsgotLLylbz/S+b2HngHKnpmVQP8mbCqB7UDvbTlYmITuTrxRs5duYKWq2WQD9Ppr89CKW7k+ECv6FzLQ+61PLEw94SgOjUbFYcu8axmHRdmeca+tCppge2lmZcSszkh32RRKfm6I6/0jqAej6OONtYkJtXQFhCJksORXEtPbfcrwcgLCyKf/89SGRkAunpmYwd24+GDavrjo8Y8WmJ5z377JN06dIMgMTEVFas2MalSzHk5xcQEhLEoEEdcXS0LZdruN22TQc5c/ISiQkpmJubERDoTdfebfDwdNGV0Wq1bN6wn4N7T5GTo6aKv5I+/duj9HIrVp9Wq+Wnb/8k7HwkQ0b2IqRecHlezl3d77XUrNe7JZ736rCuvNCvTXmG+tBk1k8lotVqefnll1m1ahWpqakcP36c+vXrGzqs/yQ3N48gfyWd2jVg2szfix338XJlzIvdUHo4o9Hks/qf/UyevoSFc17HyaHozfPLr/8kK1vNlIkDcbC3Ycfe03w6ZyVeni5UC/Qq70u6r80/v0VBoVb3+EJ4LE+P+5pe7RsYMKrSmT7/D8KvJjD1jf64udizcccJXn1/Icu/fgMPV0di4pJ56Z3v6NWhCaMGdsDO1oqI6EQszI3jpZqcpWHp4SjiVWoAngx2451O1Xlz9RmiU3PoW8+LXqFezNsZTmx6Ls808GFq15qMXXmS3LxCAMKTsth1OZnrmWrsLc14rpEvU7rV5JXlJ7jt11tuNJo8fH09adWqLt98s7rY8ZkzX9V7fPr0FRYt+odGjWoAoFZrmDnzd/z8PJg4cSAAq1fvZt68Vbz33hBMTMr3E+LK5RhaPlEfX38lhQWFbFy3lx+/XsVbk1/EwtIcgB1bDrN7+1H6D+6Cu4czW/89wIL5q5j4/nCsrCz06tu9/ZhRfsrd77X0z+L39MrvOxrGJ/P+5KmWIQaK+L+Trp9KZOPGjSxatIh169YRFxdHSEjF+4O9qUmDYIY+155WTWuXePzJ1nVpEFoVL08X/P08GPVCZ7Jz1ERcTdCVOX8xhl6dm1Gjmi9eni4M7NcWW1srwiNiy+sySsXN2R5PVwfdtmnPWQJ93WjVsJqhQ3sgueo8tu87y6vDutIgJBA/bzdGDeqAt6cLf244CMC3yzbRslENxr3YlRpVvfFRutC6SU1cnOwMHH2RI1FpHItOJzY9l9j0XH45EkNuXiHVPYri6xGiZNWJaxyITCUqNYe5O8KxNDOhTdVb39Q3X7jOufgMrmdquJKcza9HonG3s8TDztIg1xQaWpV+/droEo87OTra6W3Hj1+iRg1/3G+0cF26dI2kpHSGD++Or68Hvr4eDB/enYiIOC5cuFqOV1Jk5Jinadw8BKWXG96+HvQf3Jm01Axioote+1qtlj07jvFUp2aE1g9G6e3Gc893IS8vnxNHzuvVFRuTyO7tR+g/uHO5X8e9PMhrydXZXm/bdfA8jUKD8FG63Kd2YWwqVaISHh6Ol5cXLVu2RKlUYmZW9t9SNRpNmdf5sPLy89mw9Si2NlYE+Xvq9tepWYVd+8+QkZlNYWEhO/adJi+vgNDagQaM9sFo8vJZufEwg3o2rzDfFAoKCikoLMTSQv/vztLCjJPnIiksLGTfkQtU8XbjtSk/0eWFaQx/62t2HjhroIjvzUQBrYNcsDI3ISwhE097S1xsLDhxWzdQfqGWs3EZ1PQsOdGyNDPhqeruxKtyScoyvtfOndLTszh9Opwnnqir25efn49CAWZmprp95uamKBQKLl2KNkSYenJzi1q/bGysAEhJTidDlUX1mv66MmbmZgRV8+XqbV9SNJo8fl28nj7Ptsfeofy7sO7lfq+lOyWnZrD3yAV6dWxcThGWrZtdPw+7VVSVJlEZNmwY48aNIyoqCoVCQUBAAFqtls8//5ygoCCsra2pV68eq1at0p1TUFDAiBEjCAwMxNramho1ajBnzpxi9fbp04cZM2bg7e1N9erV73xqgzl4NIy+Qz+h9wvTWPPPfj6ZPATH295w3n39WQoKC+k/8jN6vfAx8xas5f03B+BdAb5x/LPzFOmZOQzo3tzQoTwwWxtLQmtW4afft3E9WUVBQSEbth/n7MUYklIzSE3PIjtHw5I/dtKiYXXmfjicts3r8PaMXzh25oqhw9ep4mzNr8Mas2J4U15pHcinmy8Sk5aDk3VRt0JaTp5e+bScPJxszPX2danlwa/DGrP8xSY09HXiw38ukG+Ifp9S2rfvNJaWFnqtL1Wr+mBpacGqVTtQq/NQqzWsXLkdrVZLenqWAaMtaj1Z++cOAoJ8UHoXtWplqIpisrsj+bCzt9EdA1j75w78A72pU9f4Wizv91q60z/bjmFrbUm7FsY5/u5+bnb9POxWURlHx3c5mDNnDlWrVuWHH37g8OHDmJqa8r///Y8///yTb7/9luDgYHbt2sXzzz+Pu7s7bdu2pbCwEF9fX1asWIGbmxv79u3jpZdewsvLi/79bw3e3Lp1Kw4ODmzevBmttuQ3W7VajVqt1j1WqVSP/Jrr1Qnk689eIT0jm41bjzJj9gpmTxuFk2PRt9vFv28lMzOH6ZOH4uhgw/7D55k+ewVfTB1OYBXP+9RuWL/8vZ/2LWrj5e5o6FBKZeob/Zk29w96vDgDUxMTalT1pnObely4EkvhjQ/qNs1qM7B3awCqB3lz+kIUf244SMOQIEOGrhObnsuEP09ja2FGi0AXXmtblf+tu63L4I6XgEIBd74sdl1O5uS1dJxtLOhd14u32gfz7tqz5BUYd7KyZ88pmjevjfltY4bs7W145ZU+LFv2L1u3HkGhUNC0aW38/T0N/uGwZuVW4mOTGD1+QLFjxSLTovvaffb0ZS5fjGL82y888hj/q3u9lu60dstROretj6WFeQk1CWNXaRIVR0dH7O3tMTU1RalUkpWVxcyZM9m2bRstWrQAICgoiD179vD999/Ttm1bzM3N+fDDD3V1BAYGsm/fPlasWKGXqNja2vLjjz9iYWFR7HlvmjFjhl5d5cHKygJvpSveSldqBfsxYvwc/t1+jOf6tCE2PoW1/x7iuy/G4u/nAUCQv5IzF6JYt+kQ40b2LNdYSyM6LoWdh8NY9OlIQ4dSar5ernw34yVycjVkZefi5uLA5M9/xdvTGScHG0xNTQi88fu4KcDXnZPnyn+sw93kF2pvDKZVE56URTV3W3qEeLL6ZBwATjbmpN7WquJoZU76Ha0s2XkFZOcVEKdSczExk6VDGtEswIU94cnleSmlcvFiNPHxKbzySu9ix0JCAvn001fIyMjG1NQEGxsr3nhjHk2bOpV/oDesWbmVc6fDGf36AJyc7XX7b3bjZKiycHC81SWXmZmNvb0NAOEXo0lJSmPKpPl6dS5duJbAqj688vpz5XAF93av19Ltjp+N4Oq160ybNNBAkT68yj6YttIkKnc6d+4cubm5dOzYUW+/RqOhQYNbs0i+++47fvzxR65evUpOTg4ajabYTKHQ0NB7JikA7777LhMmTNA9VqlU+Pn53eOMsqfVQl5eAQBqTdEHh+KOGQkmJgrdN3tj9eu6A7g529OpVcVsxgWwtrLA2soCVWYOB45f4tWhXTE3N6N2sC9Xr13XKxsVm4TSw8kwgT4ABWBuakJChpqUbA31fByJSM4GwMxEQR0ve5YcuvdYDYUCzMt5dkxp7d59En9/JX5+d29tvPlBf/58JBkZWdSvX/7dJlqtlr9WbuPMqcu8/Fp/XNz0Wx1dXB2xd7DlUthVfG5cS35+AVcux9Ct1xMAPNmxKU1bhOqdN3PGYnr2a0ftkKrlcyEPqKTX0u3Wbj5CzWo+VDfCmYwPSqYnV1KFhUVTJdevX4+Pj4/eMUvLotkHK1as4I033uCrr76iRYsW2Nvb88UXX3Dw4EG98ra29x9oZmlpqau3LOTkqomNv7WmSEJiKuGRcdjbWeNgZ8Py1bto1rgGLk72ZGRms27TYZJSVLo1Uvy83fBWujBvwVpGPt8Jezsb9h85z/HTV5g6aVCZxVnWCgsL+W3dAQZ0b6o3eLGiOHDsIlqtFn8fd6Ljkpm3aAP+Pm707NAIgOf7tmHyF7/RoE4gjUKDOHDsInsOXeCb6aMMHHmRwY19ORaTTlKmGmtzU56o6kodLwc+3ngBgHVn4nmmvjdxqlzi0nN5ur436vxCdoUnAeBpb0mrIFdOXEtDlZOPq60Ffet5ockv5Fh0mkGuKTdXQ2Jiqu5xUlIaUVEJ2Npa4epa9CGfk6PmyJEwnnvuqRLr2LPnFF5ertjb2xAefo3ffttCx45NUCpdy+UabrdmxVaOH73A0FG9sbKy0I07sbKywNzCHIVCQet2Ddm26RBu7s64uTuzbdNBzM3NqN+4aC0mewfbEgfQOjnbF0t8DOV+ryWAzOxctu49zevDuxsw0ocnLSqVVO3atbG0tCQqKoq2bduWWGb37t20bNmSMWPG6PaFh4eXV4j3dCk8lrc/XqR7/MPSfwHo0KY+40b2IDo2iS0zT5CekY2DvQ3Vg7z5YupwXTePmZkpH739PD//tpmpX/xKTq4Gb08X3hzdl6YNjGdA8J12HgojJj6VQT1bGDqU/yQzO5dvlvxLYlI6DvY2PNmiDqNf6KxLutq1qMPbo/uweNUOZi5YSxUfd2a8M5j6tQMMG/gNTjbmjG9XFWcbc7I1BUSmZPPxxgucvFY05mr1yTgsTE14qVUAdhZmXLqeyYcbLujWUNEUFFJbaU/PECW2lqak5+RxNj6Dd/4+R3puvkGuKTIyji+++E33+PfftwHQsmUII0b0AODQofOAlqZNa5VUBfHxKfzxx06ysnJwc3Oke/eWdOrU5JHHXpL9e04C8P3cFXr7+w/uTOPmRUsytOvQhLy8fFav2EpOdi5+AV6MGvtMsTVUjNn9XksAm3edQquFTm3qGTBS8bAU2ruN/nwMzZ49m9mzZxMZGQnA//73P7777ju++uorWrdujUqlYt++fdjZ2TF06FDmzJnDBx98wIoVKwgMDGTp0qXMnTuXwMBATpw4ARTN+klLS2PNmjWlikWlUuHo6Mi6IxHY2tnf/4QKokmg8c8YKq3whExDh1Cm3l53ztAhlLkXmvncv1AFk5ydd/9CFUxb/+Ir31ZkmRkqWtXxJT09HQcHhzKv/+bnROtPN2Fm9XBTxPNzs9jzTqdHFuujVGlbVAA+/vhjPDw8mDFjBleuXMHJyYmGDRvy3ntFKxq+8sornDhxgueeew6FQsHAgQMZM2YMGzZsMHDkQgghKovK3vVTqVpUjIm0qFQc0qJi/KRFpWKQFpXSufk58cRnm8ukRWX32x2lRUUIIYQQZUtBGcz6KZNIDEMSFSGEEMKImSgUmDxkpvKw5xtSpVlCXwghhBAVj7SoCCGEEEZMFnwTQgghhNGq7LN+JFERQgghjJiJomh72DoqKhmjIoQQQgg93377LXXr1sXBwQEHBwdatGiht4aYVqtl6tSpeHt7Y21tTbt27Th79qxeHWq1mnHjxuHm5oatrS29evUiJiam1LFIoiKEEEIYM8Wt7p//upV2frKvry+ffvopR44c4ciRIzz11FP07t1bl4x8/vnnzJw5k/nz53P48GGUSiUdO3YkIyNDV8f48eNZvXo1y5cvZ8+ePWRmZtKjRw8KCgpKFYskKkIIIYQRuzmY9mG30ujZsyfdunWjevXqVK9enU8++QQ7OzsOHDiAVqtl9uzZTJ48mX79+hESEsLixYvJzs7m119/BSA9PZ2FCxfy1Vdf0aFDBxo0aMCyZcs4ffo0W7ZsKVUskqgIIYQQ4q4KCgpYvnw5WVlZtGjRgoiICOLj4+nUqZOujKWlJW3btmXfvn0AHD16lLy8PL0y3t7ehISE6Mo8KBlMK4QQQhgxxY3/HrYOKFqW/3aWlpZYWlqWeM7p06dp0aIFubm52NnZsXr1amrXrq1LNDw9PfXKe3p6cvXqVQDi4+OxsLDA2dm5WJn4+PhSxS4tKkIIIYQRuznr52E3AD8/PxwdHXXbjBkz7vq8NWrU4MSJExw4cIDRo0czdOhQzp27da+wO6c8a7Xa+06DfpAyd5IWFSGEEKKSiI6O1rsp4d1aUwAsLCyoVq0aAI0bN+bw4cPMmTOHt99+GyhqNfHy8tKVT0xM1LWyKJVKNBoNqampeq0qiYmJtGzZslQxS4uKEEIIYcQedsbP7QvG3ZxufHO7V6JyJ61Wi1qtJjAwEKVSyebNm3XHNBoNO3fu1CUhjRo1wtzcXK9MXFwcZ86cKXWi8kAtKnPnzn3gCl977bVSBSCEEEKIuzPEEvrvvfceXbt2xc/Pj4yMDJYvX86OHTvYuHEjCoWC8ePHM336dIKDgwkODmb69OnY2NgwaNAgABwdHRkxYgRvvvkmrq6uuLi48NZbbxEaGkqHDh1KFcsDJSqzZs16oMoUCoUkKkIIIUQFl5CQwAsvvEBcXByOjo7UrVuXjRs30rFjRwAmTZpETk4OY8aMITU1lWbNmrFp0ybs7e11dcyaNQszMzP69+9PTk4O7du3Z9GiRZiampYqlgdKVCIiIkpVqRBCCCHKholCgclDNqmU9vyFCxfe87hCoWDq1KlMnTr1rmWsrKyYN28e8+bNK9Vz3+k/j1HRaDSEhYWRn5//UAEIIYQQ4u4MseCbMSl1opKdnc2IESOwsbGhTp06REVFAUVjUz799NMyD1AIIYSozMpyMG1FVOrpye+++y4nT55kx44ddOnSRbe/Q4cOTJkyhXfeeadMA3zcrbt4HUubHEOHUWbq+joaOoQydykl09AhlKkdPyw1dAhlrorbaEOHUOY27rli6BDKnO3guoYOoUzlZGbcv5B4aKVOVNasWcPvv/9O8+bN9TK02rVrEx4eXqbBCSGEEJWdIWb9GJNSJyrXr1/Hw8Oj2P6srKwK3bQkhBBCGCNDDKY1JqUeo9KkSRPWr1+ve3wzOVmwYAEtWrQou8iEEEIIUemVukVlxowZdOnShXPnzpGfn8+cOXM4e/Ys+/fvZ+fOnY8iRiGEEKLSUtzYHraOiqrULSotW7Zk7969ZGdnU7VqVTZt2oSnpyf79++nUaNGjyJGIYQQotKSWT//QWhoKIsXLy7rWIQQQggh9PynRKWgoIDVq1dz/vx5FAoFtWrVonfv3piZyc2YhRBCiLJkoijaHraOiqrUmcWZM2fo3bs38fHx1KhRA4CLFy/i7u7O33//TWhoaJkHKYQQQlRWZdF1U5G7fko9RmXkyJHUqVOHmJgYjh07xrFjx4iOjqZu3bq89NJLjyJGIYQQQlRSpW5ROXnyJEeOHMHZ2Vm3z9nZmU8++YQmTZqUaXBCCCGEqNgLtj2sUreo1KhRg4SEhGL7ExMTqVatWpkEJYQQQogiMuvnAahUKt2/p0+fzmuvvcbUqVNp3rw5AAcOHOCjjz7is88+ezRRCiGEEJWUDKZ9AE5OTnrZmFarpX///rp9Wq0WgJ49e1JQUPAIwhRCCCFEZfRAicr27dsfdRxCCCGEKEFln/XzQIlK27ZtH3UcQgghhChBZV9C/z+v0JadnU1UVBQajUZvf926dR86KCGEEEII+A+JyvXr13nxxRfZsGFDicdljIoQQghRdkwUCkwesuvmYc83pFJPTx4/fjypqakcOHAAa2trNm7cyOLFiwkODubvv/9+FDEKIYQQlZZCUTZbRVXqFpVt27bx119/0aRJE0xMTPD396djx444ODgwY8YMunfv/ijiFEIIIUQlVOoWlaysLDw8PABwcXHh+vXrQNEdlY8dO1a20QkhhBCVnCz4Vko1atQgLCyMgIAA6tevz/fff09AQADfffcdXl5ejyJGcYfju48Scf4KaUmpmJqZofRT0qxjC5zcnEssv2vtds4fPUeLzq2p26Kebn92RhYHNu8jJjyaPE0eTq5ONHiiEUF1DL/C8Lylm9mw8xSXryZiZWlO49AA3hvdk2pVPHVlxn/yCys3HNY7r0Ftf9b98EZ5h1uiC2FRrP9nPxFX40lLy2T8uGdo3KiGXplrsUksX7GNC2FRaLVafLzdGDe2H26ujroyly7HsPKPHYSHx2JqakKVKp5MenMAFhbm5X1JvD2qG++81E1vX0Kyippd3tMrM7RvK5zsrTl69ioTP/+dC1fidcdnvTuAtk1roHRzJCtHzaFTEUyd9xeXrhZf8fpR61DdjXreDnjYWZBXqCUiOZu1ZxNIzLw1SWBQQ2+a+eu/tiJTspm1M0L32N7SjN4hntTwsMXSzJTETDWbw5I4GauivA1s4c/AlgH4uFgDcCk+g282X2LXhUQAbCxMebN7LTqEKHGyteBaSjZLd0fw2/6rujo+fKYuLYPd8HC0Iludz/HIVL5cf54riZnlfj0Aly5Gs2XTYaKj4klPz+Kl0X2oVz9Yd1ylymLNnzu5cC6S7Gw11YJ96T+gAx6et35ve3ad5Mjh80RHJZCbq+GLWeOwsbEyxOWUWll03VTgPKX0icr48eOJi4sDYMqUKXTu3JlffvkFCwsLFi1aVNbx6WnXrh3169dn9uzZj/R5jF1sZCx1moTg7uOBtlDLoa0HWL/0b/qPHYT5HR9eEeevkBiTgI29bbF6tq3egiZXQ5eB3bGyseLy6UtsWbWJfi6OuHm5l9fllOjA8XCG9mtN/ZpVyC8o5LMF6xn0xnfsWPYONtaWunJPNqvJzPcG6R6bm5saItwSqdUaqlTxpM0T9Zgz/49ixxMSU/n4kyW0bVOPp/u2wcbakmuxSZib33pZXrocw+dfLadn95YMeb4zZqamREUnGPTb0fnwWPqMnad7XFCg1f379SEdGDPoScZ+tIzwqETeGt6FP+ePo+kzH5GZrQbgxIVoVm48THR8Ks4ONrzzUnf+nD+Wer2nUFioLfZ8j1I1Nxt2X0khKjUHEwX0qOPJ6Fb+zNhyGc1t13UuPoNfj8XqHhfcEecLjX2wMjNhwYFostT5NPJzZFhTX77cfoVr6bnldj0A8em5fLn+PFFJWQD0aeLH1y82oe/MnVxOyOTd3nVoVs2Nib8e51pKNq1quDOlXyiJqly2ni1KFs/GpLH2WAxxqTk42lgwrnN1Fr7UnPafbKGcf0UAaDR5+Pq606JlCAu+/0vvmFar5YdvVmNiasrLY/piZWXJ1i2HmTt7Be9PfRFLSwtdHbXrBFK7TiB/rd5V/hch/rNSJyqDBw/W/btBgwZERkZy4cIFqlSpgpubW5kGJ0rW/YWeeo/b9WnPki9+4nrsdbwDvHX7s1SZ7P1nF91e6MmGX9YXqychOp4nerTDw7eolaJh28acOnCCpLjrBk9Ufpn5it7jWe8Oom7P/3EqLIbm9avq9ltYmOHh6lDe4T2QenWrUa/u3VunVq7aQb26VRn4XHvdPg8P/W/uy37dTKcOjenVo6Vun1LpUvbBlkJ+QSGJyRklHntl4JPM/Plf1m0/CcDoqUu5+O90nuncmEWr9wKw+Mb/AaLjUvjk27Xs+e09qni5Enkt6dFfwG2+2xel9/iXo9eY3r0mfk7WhCdn6/bnF2rJUOfftZ4AF2tWnIgjKjUHgE1hSbSr5oqfk1W5Jyrbz+m3TM3ecIGBLf2p7+/M5YRM6vs7s+ZwNIfCkwFYcSCK55r7E+LnpEtUVhy49XO5lprD7A0X+Putdvi42BB928+lvNQJCaJOSFCJxxITU4mIiGPylBfx9i76DBowqCPvvPU1Rw5foFXroiUznurQGICLYVEl1mPMKvusn/+8jspNNjY2NGzYsCxiEf+RJrfom6rVbS0N2kIt2/7cQr1WDXDxcC3xPGUVb8LPXKJKsD+WVpaEn71MQX4BXgE+5RJ3aaiyij4AnBxs9PbvP36Zuj3+h4OdNS0aVOXtl7rj5mxviBBLpbBQy4lTl+netTmfffkbV6/G4+7uRM/uLXXdQ+mqLMKvxNKqRQgfTltEQmIa3l6uPPt0O2pU9zNY7EF+7pz75xM0mjyOnr3KR9/8zdVryfj7uKJ0c2TbgQu6spq8fPYeu0zTukG6ROV2NlYWDOrZnMhrSVxLSC3PyyiR9Y0WuWyN/jIL1dxsmdatBjl5BVxOymb92QQybytzJTmbhr6OnIvPJCevgPo+DpiZKLh0o1XDUEwU0KWeNzYWphy/WvTzPRaRwlN1lKw6FE2iKpdmVV0JdLdj+l9nS6zD2sKUfk2qEJ2cRXxaTnmG/0Dy84t+D7e3ppqYmGBqakr45RhdolKRSdfPA5gwYcIDVzhz5sz/HMyDKCwsZNKkSfz4449YWFjwyiuvMHXqVN1z//zzz1y5cgUXFxd69uzJ559/jp2dHQCLFi1i/PjxLFq0iEmTJhEVFcUTTzzBTz/9hJ9f0Rv/1KlTWbNmDaNHj2batGkkJyfTvXt3FixYgJOTE7t27aJ9+/ZER0ejVCp1cb355pscPnyYXbvKt0lRq9Wy/9+9KKt44eJ5KyE5sfcYJiYmhDS7+4u0w7Od2LJyE4s/X4iJiQlm5mZ0HtANRxfHu55jCFqtlg/nraFp3SBqBt0aB/Vk81r0eLI+vkoXomKT+eLHf+j/2tdsWPgWlhYPnYM/UipVFrm5Gtat388zT7dlwLNPcvL0FebMX8V7bz9PrZr+XE9MA+DPNbsZOKA9/lU82bP3NDM+/4VPp71kkJaVo2cjGT1lKeFRibi72vPW8C78u/BNWjz3CZ43Wraup+i3tiSmZOB3R6wjnnmCqeP6YGdjSVhEPH3Hzicv3/BrMPUJ9SQ8KYu4DLVu3/mETE5cU5GanYeLrTndannw6hMBfLH9iq4LaNGhGIY19WVGj5oUFGrRFBSy8EA0yVl5BrmO6kp7lr/WGkszE7I1BYz9+QjhCUXjS6atOcPHz9Zj95SO5BUUotVq+d+KUxyNSNGrY1BLf97qURtbSzPCEzJ48fsD5BUYoN/nPpRKF1xcHfhr9W4GDe6EhaU527YcQaXKQpVu2ESxrMgS+g/g+PHjD1RZefwgFi9ezIQJEzh48CD79+9n2LBhtGrVio4dO2JiYsLcuXMJCAggIiKCMWPGMGnSJL755hvd+dnZ2XzyyScsXrwYCwsLxowZw4ABA9i799a3vcuXL7NixQrWrl2LSqVixIgRjB07ll9++YU2bdoQFBTE0qVLmThxIgD5+fksW7aMTz/99K5xq9Vq1Opbb36335H6Yez5ZxfJCcn0Ht5Pt+96bCKnD5zk6Zefu+fv5PC2g2hyc+k+pBfWNtZEXLjC5hUb6TW8H66eJbfCGMLkmX9wPjyW1d+8rre/d/tbLXk1g7yoV9OPZs98xNb9Z+nWtt6d1RiVmzfybNiwOl07NwPA31/JpcsxbN1+jFo1/Sm8UebJJxvQ9omi6wnwV3L2XCQ7d5/kuWefLPe4t+w7d+tBOBw+FcGxNVMZ2L0ZR84UDS69eW03KRSgRX/fyg2H2X7wAko3B159vgM/zxhOl5EzUWvu3r3yqD1TzwtvByvm7IrQ23/82q3XalyGmujUXKZ0CaaO0o5TsUVJWffaHlibm/L1nkgy1fnU9XZgWFM/5u6OIE6lprxFXM+kz1c7cbA2p1NdLz4bWJ/nv9lHeEImLzwRSH1/Z15ZeIjY1GwaB7nqxqjsv3Sr6+3vY9fYezEJdwdLRrSryuwXGjFw/l40+YXlfj33YmpqyqiXe7NsyUYmTpiHiYmCGjX9qR0SaOjQRBmpcDclrFu3LlOmTAEgODiY+fPns3XrVjp27Mj48eN15QIDA/n4448ZPXq0XqKSl5fH/Pnzadas6MNh8eLF1KpVi0OHDtG0aVMAcnNzWbx4Mb6+vgDMmzeP7t2789VXX6FUKhkxYgQ///yzLlFZv3492dnZ9O/f/65xz5gxgw8//LBMfxZ7/tnF1bBIer3YFztHO93+uKtx5GTl8Musxbp9Wq2WA5v2cvrASQa/MYT0lHTOHjrNs2MG6LqGXJVuxF+N4+yh07Tp2a5MY/2v/jfrDzbtPcOf88fh7eF0z7Kebo74KJ2JiL5ePsE9BHt7G0xNTfDx1h/X5ePtRtjFaACcnOx0+27n7e1KcnJ6+QR6H9m5Gs5djqWqnzvrdxaNS/FwdSAh+daHu7uzPdfvGNOiyspFlZXLlejrHD4dScS2z+nRrh5/bDparvHf9HRdJSFKe+bujiA9997JkkqdT2p2Hu62lkAGrrbmtKnqyowtl4m/0RITq7pOkKsNTwS5sOJEXDlcgb68Ai1RN8aSnIlJJ9TPiSFPBDF9zRne6FqLVxcdZuf5ollAYXEZ1PJxYES7qnqJSmZuPpm5+VxNyuLk1VQOfdyFjqFK1h+PLfE5DamKv5L33h9GTo6a/PwC7O1t+HzGMvz9Pe9/cgVgwn9YS6SEOiqqChf7nfcS8vLyIjGx6AW3fft2OnbsiI+PD/b29gwZMoTk5GSysm41/5mZmdG4cWPd45o1a+Lk5MT58+d1+6pUqaJLUgBatGhBYWEhYWFhAAwbNozLly9z4MABAH766Sf69++PrW3xmTU3vfvuu6Snp+u26Ojo//wz0Gq17Fm/i4jzV+g5tDcOzvqDSavXq8GzowfwzCvP6TYbe1vqtWygG4ibn1f0Znxni4vCRFHsG7EhaLVaJs9cxYadp1gxZyxVvO/fwpOSnkVcYprRDq69nZmZKUGBXsTFJevtj4tPxs2tqOvN3c0RZye7YmXi41NwdTOO7jkLczOqB3gSn5zO1WvJxCel82Szmrrj5mamtGpYjUOnrtyzHoVCgYWBuuuerqukrrcDX++JJCX7/l01NhamOFmbo8otKmthWvQ2emerUaHWeG4Ep1CAhZkJZqYmWJiZcOdLvKDw/i3iCoUCCzPj/siwtrbE3t6GxIRUoq7GU7e+4ZdaKAuyjkoFY26uP/1WoVBQWFjI1atX6datG6+88goff/wxLi4u7NmzhxEjRpCXl1fsnDvd65d489jN/3t4eNCzZ09+/vlngoKC+Oeff9ixY8c947a0tMTS0vKeZR7UnvW7uHz6Ip0HdsPcwpzsjKJEzMLKEjNzM6xsrLC6Y30AExMTrO1sdGutOLk54eDiyK61O2jRqRWWNlZEXrhCTHg0XQcZfnXh975axZotR/lpxkjsbCxJvPEN3d7OCmtLC7Ky1Xz100a6tauLp6sD0XEpfPrDepwdbena1jgGz+XmakhIuNXvfz0pjatX47G1s8bN1ZFuXZsz/5vV1KxRhVq1/Dl1OpzjJy4x+Z0XgKK/t+5dW/DHml34V/GkShVPdu85RWxcMq+9+rRBrumj1/uycfdpYuJTcXe2460RXbC3tWL5uoMAfPfbdia82Inw6ESuRF9nwrDOZOfmserfIwD4+7jSr2Mjth04T3JqJl4eTrw+pAO5uXls3lvyYM5H6dl6XjT0deTHA1Hk5hdib1n0lpibV0BeoRYLUxO61nLnZKwKVW4+Ljbm9KjtSZamgFNxRa1ECRlqrmeq6V/fm7/OJJClyaeulwM1PGxZsD/5Xk//SLzRtSa7LiQSn5aDraUZ3Rr40LSqGyMXHCBLnc/By0lM7FGL3LwCYlOzaVLVlT6Nffn0xmBaXxcbutX3Zu/F66RkavB0tGLUU9XIzSvQtcKUt9xcDdev3xpsnZyUTnR0Ara21ri4OHDsaBh2dkX/vnbtOqtWbKNe/WrUqn2r+yc9PROVKovr19MAiL2WhKWVOS4uDtjaWpf3JYlSqHCJyt0cOXKE/Px8vvrqK0xMirL+FStWFCuXn5/PkSNHdN08YWFhpKWlUbPmrW+BUVFRxMbG4u1dNNV3//79mJiYUL16dV2ZkSNHMmDAAHx9falatSqtWrV6lJen59yRMwCsXbRGb3+73k9Ro0GtB6rD1NSUboN7cHDLfjb+tp48TR4OLo482bcDVaoHlHHEpbdkTdGYoWfGzdfbP/O9gTzXrRkmpgouXIll1cbDqDJz8HB1oGXDanz74VDsjGQRpysRcUz/bJnu8S+/bQHgiVZ1eXlUT5o0qsnwoV35e/0+lvyyCS+lC6+/+rTejJ4unZuiyctn2W+bycrMpUoVD96ZOAjPO6YxlxcfDyd+nPYirk62JKVmcuRMJJ2Gf0V0fNGHyJwlW7CytODLt5/Dyd6Go2cjeXrcfN0aKmp1Pi3qV+WVAe1wcrDhekoG+45fpvPIr0hKLf/FxFoHFQ3yfa2N/niGX45e41BUGlqtFi8HK5pUccLa3ARVbj6Xrmex6HA06htjNQq18P2+KHrW8eSl5lWwMDMhKUvDL0evcS6h/K/Jzd6Szwc1wMPBkoycfMLiVIxccIB9F4u6dSYsO8aEbjX5cnADHG0siE3NYdY/F3QLvmnyC2gc5MLQNkE4WJuTnKnmyJVkBs7bQ8ptC+GVp6ir8cyZ+bvu8R8ri4YjNGtRhyHDupGenskfK7eTocrCwdGOZs3r0LV7C7069uw6yT/r9ukez/ryNwCeH9qVFi1DyuEq/juFomgG18PWUVEptMbQzv+ASlrwrU+fPjg5OTF+/HgaNGjA7Nmz6dmzJ3v37uXdd9/l2rVrpKam4uTkxKJFi3jppZdo0KABc+fOxdzcnFdffbVo5sz+/UDRrJ8vv/ySFi1a8OWXX6JSqRg5ciQNGzbkt99+0z1vYWEhAQEBJCQk8NFHH/H222+X6lpUKhWOjo688ushLG3s7n9CBfFBh+D7F6pgtl82/jEvpfH8sE8MHUKZG/LeaEOHUOY27rl3d1lFNGWwcbR2lpWczAxebR9Keno6Dg5l3+V883NizG+HH/pzQp2dyTcDmzyyWB8l4+5wLIX69eszc+ZMPvvsM0JCQvjll1+YMWNGsXI2Nja8/fbbDBo0iBYtWmBtbc3y5cv1ylSrVo1+/frRrVs3OnXqREhIiN6AXCjqShk2bBgFBQUMGTLkkV6bEEIIUVn9p66fpUuX8t133xEREcH+/fvx9/dn9uzZBAYG0rt377KOUaekcSBr1qzR/fuNN97gjTf07/PywgsvFDunX79+9OvXr9j+240ePZrRo+/9LS0uLo5u3brJPY6EEEI8MpV9HZVSt6h8++23TJgwgW7dupGWlkZBQdEiTU5OTpXmHjzp6els2bKFX375hXHjxhk6HCGEEI8xE0XZbBVVqROVefPmsWDBAiZPnoyp6a0lixs3bszp06fLNDhj1bt3b3r16sXLL79Mx44dDR2OEEII8dgqdaISERFBgwYNiu23tLTUW6/EGA0bNoy0tLR7lpk6dSonTpy4Z5kdO3aQnZ3NrFmzyi44IYQQogQ37/XzsFtFVepEJTAwsMQP8g0bNlC7du2yiEkIIYQQN9y8e/LDbhVVqQfTTpw4kbFjx5Kbm4tWq+XQoUP89ttvzJgxgx9//PFRxCiEEEJUWpV9Cf1SJyovvvgi+fn5TJo0iezsbAYNGoSPjw9z5sxhwIABjyJGIYQQQlRS/2l68qhRoxg1ahRJSUkUFhbi4eFR1nEJIYQQgrIZY1KBe34ebgl9Nze3+xcSQgghxH9mwsOPMTExmltkll6pE5XAwMB7Lhxz5crjt+yzEEIIIQyj1InK+PHj9R7n5eVx/PhxNm7cyMSJE8sqLiGEEEIgXT+lTlRef/31Evd//fXXHDly5KEDEkIIIcQtZbGybKVamfZuunbtyh9//FFW1QkhhBBCPNxg2tutWrUKFxeXsqpOCCGEEBR12zzsYNpK1fXToEEDvcG0Wq2W+Ph4rl+/zjfffFOmwQkhhBCVnYxRKaU+ffroPTYxMcHd3Z127dpRs2bNsopLCCGEEKJ0iUp+fj4BAQF07twZpVL5qGISQgghxA0ymLYUzMzMGD16NGq1+lHFI4QQQojbKMrov4qq1LN+mjVrxvHjxx9FLEIIIYS4w80WlYfdKqpSj1EZM2YMb775JjExMTRq1AhbW1u943Xr1i2z4IQQQghRuT1wojJ8+HBmz57Nc889B8Brr72mO6ZQKNBqtSgUCgoKCso+ysdYIx87bOzsDR1GmbEwq8g3Ey+Zu7WloUMoUw0H9Td0CGUuxMvG0CGUObO2VQ0dQpmr5eJo6BDKVJZ5+TRTVPYxKg+cqCxevJhPP/2UiIiIRxmPEEIIIW6jUCjueY+9B62jonrgREWr1QLg7+//yIIRQgghhLhdqcaoVOSMTAghhKiIpOunFKpXr37fZCUlJeWhAhJCCCHELbIybSl8+OGHODo+XoOhhBBCCGG8SpWoDBgwAA8Pj0cVixBCCCHuYKJQPPRNCR/2fEN64ERFxqcIIYQQ5a+yj1F54EUvbs76EUIIIcTjbcaMGTRp0gR7e3s8PDzo06cPYWFhemW0Wi1Tp07F29sba2tr2rVrx9mzZ/XKqNVqxo0bh5ubG7a2tvTq1YuYmJhSxfLAiUphYaF0+wghhBDlTXFrQO1/3Up7q5+dO3cyduxYDhw4wObNm8nPz6dTp05kZWXpynz++efMnDmT+fPnc/jwYZRKJR07diQjI0NXZvz48axevZrly5ezZ88eMjMz6dGjR6kWhy31EvpCCCGEKD8mKDB5yJsKlvb8jRs36j3++eef8fDw4OjRo7Rp0watVsvs2bOZPHky/fr1A4oWhvX09OTXX3/l5ZdfJj09nYULF7J06VI6dOgAwLJly/Dz82PLli107tz5AWMXQgghhNF62NaU26c3q1QqvU2tVj9QDOnp6QC4uLgAEBERQXx8PJ06ddKVsbS0pG3btuzbtw+Ao0ePkpeXp1fG29ubkJAQXZkHIYmKEEIIUUn4+fnh6Oio22bMmHHfc7RaLRMmTKB169aEhIQAEB8fD4Cnp6deWU9PT92x+Ph4LCwscHZ2vmuZByFdP0IIIYQRK8tZP9HR0Tg4OOj2W1re/6arr776KqdOnWLPnj3Fjt05I/jmDYrv5UHK3E5aVIQQQggjdnMdlYfdABwcHPS2+yUq48aN4++//2b79u34+vrq9iuVSoBiLSOJiYm6VhalUolGoyE1NfWuZR7o+h+4pBBCCCEqBa1Wy6uvvsqff/7Jtm3bCAwM1DseGBiIUqlk8+bNun0ajYadO3fSsmVLABo1aoS5ublembi4OM6cOaMr8yCk60cIIYQwYoa418/YsWP59ddf+euvv7C3t9e1nDg6OmJtbY1CoWD8+PFMnz6d4OBggoODmT59OjY2NgwaNEhXdsSIEbz55pu4urri4uLCW2+9RWhoqG4W0IOQREUIIYQwYiaUwRL6pZye/O233wLQrl07vf0///wzw4YNA2DSpEnk5OQwZswYUlNTadasGZs2bcLe3l5XftasWZiZmdG/f39ycnJo3749ixYtwtTU9IFjkURFCCGEEHoeZDV6hULB1KlTmTp16l3LWFlZMW/ePObNm/efY5FERQghhDBihuj6MSaSqAghhBBGzISHn/lSkWfOSKJSRgICAhg/fjzjx48vl+cLC4vi338PEhmZQHp6JmPH9qNhw+q64yNGfFriec8++yRdujQDIDExlRUrtnHpUgz5+QWEhAQxaFBHHB1ty+Ua7mff8ct8vWwrJ8OiSUhSsfizkXRrW1d3XKvV8sWPG1jy1z7SM3JoWNufzyY+S80gLwNGfcvpc5GsXLuHSxFxpKRmMOWtgbRsUkt3fOnKbezYd4bryemYm5lSLdCbFwe0p2awn67MnB/+5viZcJJTMrC2sqBWjSqMGNSRKj7uhrgketb1olddLzwdrAC4mpzN0oNXORSZiqmJguEtA2ga6IKXoxVZ6nyORaXx454IkrM0ujq8HK14pU0QId4OmJuacPhqKvO3XyY1O6/cr2fbpoOcOXmJxIQUzM3NCAj0pmvvNnh4uujKaLVaNm/Yz8G9p8jJUVPFX0mf/u1RernpyvyxfDOXwq6iSs/C0tIc/0BvuvV6Ag+la7lf01PVXAn1csDD3oK8Ai1XU7JZdy6R67f9DgbU96ZJFSe9866mZDN3T6Tu8TN1vQh2t8XRygx1fiGRKTmsP59AYqaG8nbyXAS//72HS1diSU7N4KOJg2jdtDYA+fkF/LR8CwePXSQuMQVbGysahlZl1OBOuLk46NVzNiyKhb9t5sLlGExNTakWoOTT94ZiaWle7tckHlylTVTatWtH/fr1mT17tqFD+U80mjx8fT1p1aou33yzutjxmTNf1Xt8+vQVFi36h0aNagCgVmuYOfN3/Pw8mDhxIACrV+9m3rxVvPfeEEyM4J7g2Tka6gT7MLBHc158d2Gx4/OWbuHb37Yz7/3nqVrFnZk/b+KZ177mwO//w87WygAR68tVawjyV9KpXUM+nrm82HEfLzfGvtgdL09n1Jp8Vq/fx7ufLOHnueNxcihKFoODvHmqdV3c3RzJyMxh2artvPfJEhbPfwNTk/L/jpSUqWbBnghi03IB6FTbk4961eHlX46RlKEm2MOOZQevEn49C3tLM8a0q8rHvesw5tfjAFiZmfB5v1DCr2fx1qpTALzYMoBpvevw6m8nKO97tF+5HEPLJ+rj66+ksKCQjev28uPXq3hr8otY3Pjw2rHlMLu3H6X/4C64eziz9d8DLJi/ionvD8fKygIAHz9PGjSuhZOzPdnZuWz+Zx8/fvMH70wdiUk5/56qutmyLzKFqLRcTBTQraYHL7Wowhfbw9EU3PoJn0/I5PcT13SP8wv1f/ox6Tkci0knNScPGwtTOtdw56Xm/nyy5VK5/55y1XlU9VfS5cmGTP3yt2LHLl2J5YVn2hHkryQzK5evF63nf58t47vPxujKnQ2L4p1PFjOwbxvGjeiBuZkp4ZHxKIzgve5+FApFqRZIu1sdFVVFbg165LRaLfn5+YYOo0ShoVXp16+NLvG4k6Ojnd52/PglatTwx93dCYBLl66RlJTO8OHd8fX1wNfXg+HDuxMREceFC1fL8UrurkPL2rz3Sg96PFmv2DGtVsv3v+/kjWGd6PFkPWpV9Wb+B4PJyc3jj01HDRBtcU0aVGfYgA60bla7xONPta5Lw7pV8fJ0IcDPg5eGdCE7R03E1VsLKHXr0JjQ2gEoPZwJDvJm6HPtuZ6cTkJiWjldhb79V1I4FJlKTFoOMWk5/LQvkpy8AmorHcjSFDDpz9PsvJhETGoO5+MzmL/9MjU87fGwL1pUqo63I54OVny+KYyI5GwikrP5fNNFaiodaHDHN/zyMHLM0zRuHoLSyw1vXw/6D+5MWmoGMdEJQNHf2Z4dx3iqUzNC6wej9Hbjuee7kJeXz4kj53X1NG9Vl6Bqvri4OuLr50mXHq1JS80gNVlV7te04EAUh6PTSchQE6dSs/xELC42Fvg6WuuVKygsJENdoNty8gr1jh+4msaVlGxSc/K4lp7LhguJONuY42JT/q0PzRpUZ8TAjrRpVqfYMTtbK7744EXatQylio87tav7MW54Dy5eiSXhepqu3DeL/6FvtxYM6tuWQD9PfL3caNsiBAtz4/++riijraIyykSlXbt2vPbaa0yaNAkXFxeUSqXeqOL09HReeuklPDw8cHBw4KmnnuLkyZO648OGDaNPnz56dY4fP143zWrYsGHs3LmTOXPm6DLVyMhIduzYgUKh4N9//6Vx48ZYWlqye/duwsPD6d27N56entjZ2dGkSRO2bNlSDj+JspGensXp0+E88cStbpP8/HwUCjAzuzVFzNzcFIVCwaVL0YYIs1SuxiaTmKyiXbOaun2WFua0bFCVQ6cjDBjZf5OXn88/W49ga2NFkL+yxDK5uRo27TiO0sMZdzeHEsuUJxMFPFndHSszU87FlfyBbGtpRqFWS6a6KOG3MCt6u8wruPWhqMkvpKBQS4i346MP+j5yc4tu0GZjU9Qil5KcToYqi+o1/XVlzMzNCKrmy9WI2BLr0KjzOHzgDC6ujjg625dYpjxZmRe9zWfnFejtr+pmy9TO1Xnnqao8W88LO4u7Txe1MFXQxM+J5CwNaTnl30VXWlnZuSgUCl3Lamp6JucvxeDkaMurk7/n6ZEzGP/Bj5w+H2nYQB9QWa5MWxEZbSq5ePFiJkyYwMGDB9m/fz/Dhg2jVatWdOjQge7du+Pi4sI///yDo6Mj33//Pe3bt+fixYu6Ozvey5w5c7h48SIhISF89NFHALi7uxMZGQkUzQ3/8ssvCQoKwsnJiZiYGLp168a0adOwsrJi8eLF9OzZk7CwMKpUqfIofwxlYt++01haWui1vlSt6oOlpQWrVu2gX7+2gJZVq3ag1WpJT88yXLAPKPHGN1WPO/qg3V0ciI5PMURI/8mBo2HMmLMStSYPFyc7ZkweiqOD/hihtf8e4sdfNpGr1uDn7caMyUMxNzPcSzfQ1YZ5AxpgYWZCjqaAKWvPcjUlu1g5c1MFI1sHsu1CItmaog/Jc3EZ5OQVMKp1IAv3RqIARj0RiKmJAldbi3K+En1arZa1f+4gIMgHpXfR+JMMVdFrwe6O34mdvQ1pKfrJ2b5dJ/jnr11oNHl4eLowauwzel8EDKV3HSVXkrOJz7h1l9wLiZmcjFWRmpOHi405XWp68EpLf2btiqDgti6glgHO9KjtiaWZCQkZar7ff5WC8u73KSWNJo8Fv2yifeu62N5IOOMSipZwX7JiGy8P6UK1AC827TzBWx/9zMKZ4/C9bbyRMD5Gm6jUrVuXKVOmABAcHMz8+fPZunUrpqamnD59msTERN09Cr788kvWrFnDqlWreOmll+5bt6OjIxYWFtjY2OjuV3C7jz76iI4dO+oeu7q6Uq/ere6HadOmsXr1av7++29effXVYueXRK1W691OW6UqvybhPXtO0bx5bcxva+K0t7fhlVf6sGzZv2zdegSFQkHTprXx9/esWH2Zd4RadLMrw4TyX9SvE8g3n49Gpcpmw7ajfDL7d+Z+8hJOjna6Mk89UdRFlJKawap1e/lk9u/M+mgkFhaGGQAYnZrDS8uOYmdlxhPV3Hi7cw0mrDyll6yYmih4v1stTIA52y7r9qfn5PHRuvOMb1+Nvg180GphW1giFxMyKHiAdRsepTUrtxIfm8To8QOKHSv2J6Wl2HzPBk1qEVzTnwxVFju3HmbZz2sZ88ZAvdddeesXqsTLwZL5tw2SBTgRe+v9Jz5DTXRaLv/rGExtTztOx2Xojh2LSefi9SwcLM1oV82VFxr7Mn9PZLHxLMYiP7+Aj2evoFCr5fWRPXX7C2/8bfXo2ISuTzYCIDjQm+Onw9mw7RijBncySLylUYHe1sqcUXb9QFGicjsvLy8SExM5evQomZmZuLq6Ymdnp9siIiIIDw8vk+du3Lix3uOsrCwmTZpE7dq1cXJyws7OjgsXLhAVFfXAdc6YMUPv1tp+fn73P6kMXLwYTXx8Cm3aFB/nERISyKefvsKsWa8xZ87rjBrVk9TUTN04FmPm4VrUkpJ4xxiApNQM3F0M3y3yoKysLPBRulKruh8TXumDqakJG7cd0ytja2OFj5crobUD+N+E54iOTWLv4fN3qfHRyy/UEpuey8WETBbujSQ8KYt+DXx0x01NFHzQvRZKRysm/Xla15py09GoVF74+TBPf7efvt/t49ONYbjZWRKfnlvel6KzZuVWzp0O5+Vx/XG6rbvG/kZLys2WlZsyM7Oxt7fR22dtbYm7hzNB1Xx5YUQvEhNSOHPy0qMP/i76hiipo7Tn231XSc+991i7DHU+qdka3O5o1crNLyQpS8OVlGwWH47Gw86SUC/Dd2eVJD+/gA9nLicuMZUv3n9R15oC4OpUlPj7+3ronVPFx53EpLTyDPM/ubmOysNuFZXRtqiYm+t/W1QoFBQWFlJYWIiXlxc7duwodo6TkxMAJiYmxVbVy8t78H5VW1v9Zt6JEyfy77//8uWXX1KtWjWsra155pln0GgefJreu+++y4QJE3SPVSpVuSQru3efxN9fiZ/f3e9UefMN9/z5SDIysqhfv9ojj+th+Xu74uHqwM5DYdStUfRz1OTls+94OB+M7WXg6P47rbZovMq9C0HeHeMNDElBUTcP3EpSfJyseXPVKVT3+IC8eay+nxNONubsu5JcHuHq0Wq1/LVyG2dOXebl1/rj4qY/TsbF1RF7B1suhV3F58ZrKD+/gCuXY+jW64n7VA4F+Yb5PfUNVRKqtOebfVdJeYBp3zbmpjhZm9/z9wVFv2szI5wlczNJuRafzMwpI3C8I4lUejjj6mxPdGyS3v6YuGSaNgguz1DFf2C0icrdNGzYkPj4eMzMzAgICCixjLu7O2fOnNHbd+LECb3kx8LCgoKCB3sT2b17N8OGDaNv374AZGZm6sazPChLS8v73k67NHJzNSQm3rp1dlJSGlFRCdjaWuHqWvRmm5Oj5siRMJ577qkS69iz5xReXq7Y29sQHn6N337bQseOTVAaYO2HkmRmq4mIua57HBWbzOmLMTg72OCrdOHl59oye/FmgvzcCfJzZ/bizVhbmfN0p0YGjPqWnFw1sbeNl4lPTCU8Mg57O2sc7Gz4dfVOWjSqiYuzPaqMbNZtOkRSioonmocAEJeQws59Z2hUrxqODjYkpahY8dceLCzMDPbmOqJVAIciU0jMUGNjbsqTNTyo5+vEu6tPY6KAKT1qEexhz+Q1ZzBRgPONGSIZufm67oLOtT2JSskmLSePOl4OjG1XlT+OXSMmNafcr2fNiq0cP3qBoaN6Y2VloWs5sbKywNzCHIVCQet2Ddm26RBu7s64uTuzbdNBzM3NqN+4aE2c5KQ0Th4Lo3rNAGztrFGlZ7Jj8yHMzc2oWSeo3K+pX6iShr6O/HQoGnV+AfaWReNkcvIKyS/UYmGqoHMND07FqVDl5uNiY063Wh5kaQo4c6Pbx8XGnPreDly8nkWmJh9HK3OequZKXmEh5xMyy/2acnLUXLvttRSXmMrliKLXkpuLPVO/+o1LEbFMf+cFCgsLSUktug57O2vMzc1QKBQ81/sJFv++lar+SqoFePHvzuNEXbvOlDeLd/UZm8o+PbnCJSodOnSgRYsW9OnTh88++4waNWoQGxvLP//8Q58+fWjcuDFPPfUUX3zxBUuWLKFFixYsW7aMM2fO0KBBA109AQEBHDx4kMjISOzs7O45CLdatWr8+eef9OzZE4VCwfvvv09hYeFdy5eHyMg4vvji1noCv/++DYCWLUMYMaIHAIcOnQe0NG1aq6QqiI9P4Y8/dpKVlYObmyPdu7ekU6cmjzz2B3XyfBR9xt66P8T7c4rWi3muW1Pmf/A8417oQK46j0lfrCQ9I5uGdfxZOWeMUayhAnAxPJZJH/2se/z9ko0AdGxbn9dG9iTmWhIf71yOKqOoG6F6VR++mjqCAL+i5mkLczPOXLjK6g37yczMxcnJltCaAcz6eJTeGJby5GxjwTuda+Jia0GWJp8rSVm8u/o0R6PS8HSwpFXVokGJC17QTxYnrDzJyZh0APxcrBnZOhB7KzMSVLn8ciiKVceuFXuu8rB/T9Fswe/nrtDb339wZxrfSBjbdWhCXl4+q1dsJSc7F78AL0aNfUa3hoqZuRkR4dfYs+MYOdm52NnbEFjNlzETBmJ3xzf78tAqsOi9bGyrAL39y49f43B0OoVa8HKwpJGfH9bmpqhy8whPymbpkRjUN2Zj5RdoCXK1oU1VV6zNTclU53MlOZt5uyPJ1JR/K1HYlWtMmPqT7vG3izcA0LltA4b2f4p9Ry4AMGri13rnzZw6nPo3ksVnurdEo8njm8X/kJGZQ5C/ki/eH4aPkXwxu5fKvjKtQvsgdx4qZyUtxtanTx+cnJxYtGgRGRkZTJ48mT/++IPr16+jVCpp06YNM2bM0HWnTJkyhe+//57c3FyGDx9OXl4ep0+f1nUZXbx4kaFDh3Ly5ElycnKIiIggMjKSJ598ktTUVF03EkBkZCTDhw/nwIEDuLm58fbbb7Ny5Uq9GEu7Mq1KpcLR0ZEFO89hY2ecfb7/Ra863oYOocwdu5pm6BDK1Lt/nbl/oQpmQIvyGfNVni4kln8L06P2fN3H6/0hK0NFx4b+pKen4+BQ9mPjbn5O/LTr/EN/TmRnZjC8Ta1HFuujZJSJSmUgiUrFIYmK8ZNEpWKQRKV0bn5O/Lz7QpkkKi8+UbNCJioVrutHCCGEqEzKYmXZijtCpWJ3WwkhhBDiMSctKkIIIYQRk1k/QgghhDBalX3WjyQqQgghhBGr7C0qFTnJEkIIIcRjTlpUhBBCCCNW2Wf9SKIihBBCGLGyuKlgBe75ka4fIYQQQhgvaVERQgghjJgJCkwesvPmYc83JElUhBBCCCMmXT9CCCGEEEZKWlSEEEIII6a48d/D1lFRSaIihBBCGDHp+hFCCCGEMFLSoiKEEEIYMUUZzPqRrh8hhBBCPBKVvetHEhUhhBDCiFX2REXGqAghhBDCaEmLihBCCGHEZHqyMKjCQi0FhVpDh1FmHqdrucnC5PFqeMzOzjd0CGWuIjdr342V2eP1dweP3/tDgbZ8rsdEUbQ9bB0V1eP3ShBCCCHEY0NaVIQQQggjJl0/QgghhDBaMutHCCGEEMJISYuKEEIIYcQUPHzXTQVuUJFERQghhDBmMutHCCGEEMJISYuKEEIIYcRk1o8QQgghjFZln/UjiYoQQghhxBQ8/GDYCpynyBgVIYQQQhgvaVERQgghjJgJCkwesu/GpAK3qUiiIoQQQhgx6foRQgghhDBS0qIihBBCGLNK3qQiiYoQQghhxCr7OirS9SOEEEIIoyUtKkIIIYQxK4MF3ypwg4okKkIIIYQxq+RDVKTrRwghhBDGS1pUyohCoWD16tX06dOnXJ7v4sVoNv17kKtXE0hPz2T0mL40aFBddzw3V8Off+7kxPGLZGXl4urqwFPtG9OuXQMAsrJy+PuvPZw7F0lKqgo7O2sa1K9Or95PYGNjWS7XcD/7j1/mm1+3cSosmoQkFT/PGEHXtnV1x5UtXy/xvPfH9mLs4PblFeZdnTwXwe9/7+HilViSUzP4eOIgWjetDUB+fgELl2/h4LGLxCWmYGtjRcPQqrw0uBNuLg7F6tJqtbwzfQmHTlzSq6e89W3oQ7+GPng5WQFw5XoWP+2J4EB4SrGyb3etQZ+GPszedJHfD8fo9rvYWvBq+2o0DXTGxsKMqJRsFu+NZPuF6+V2HTdt+/cgp09e4npCCmbmZgQEedOtdxs8PF10ZbRaLZv/2c/BvafIzlFTxV9J3+fao/RyAyA7K4dN6/dx8cJV0lIzsLWzpk7danTu0Qpr6/J/LR3ddYQr56+QlpSKmbkZSj8lzTu2xNnNucTyO/7ezrmjZ2nVpTX1WtTX2x9zJZqsjCzMLcxR+nnRomNLnN1LrudROnUukpVr93AxIpaU1AymvjWQVk1uvQaWrNzGjn2nuZ6cjpmZKcGB3rw4oAO1gv10ZdZvOcy2vae4HBFHdo6a1T+9h52tdblfy39SyZtUJFGpoNRqDb6+HrRsFcp3364pdnzFiq2EXYhixMieuLo6cu5cBL/+sgknJzvq1w8mLS2TtPRMnnn2Sby8XElJVrFs2b+kpWXwyui+5X9BJcjO1VCnmg8DujdjxHs/FTt+au3Heo+37j/HhBnL6dGuXnmFeE+56jyq+ivp8mRDpnz5W7Fjl67E8sIz7ajqryQzK5f5i9Yz+bNlfP/ZmGJ1rVq/D4UR3FXsekYu32wPJyY1G4Budb34/Nm6DP3xMBFJWbpybaq7UdvHgesZ6mJ1TOlVGzsrMyatPEVadh6d6ij5uG8Iw386zMWEzHK7FoDwyzG0bFMfP38lhQWFbFy7lwXzVzHxfy9iYWkOwI4th9m1/SjPPd8Fdw9ntmw8wIJ5q5j4wXCsrCxQpWeRnp5Fj75t8VC6kpai4o/lW1ClZzJkZK9yvR6A2KuxhDYNxcPHg8JCLQe37mftkr8Z+OogzC3M9cpeOX+FhGsJ2NrbFqvH3dud6nWrY+dojzonl8M7DrF26V88P34IJibl2xifq9YQ5K+kU7sGfDRzebHjvl6uvPpiD7w8nVFr8vhj/X7e+WQxi+e+gZND0bWp1Xk0qRdMk3rBLPxtc7nG/7Bk1o+okEJDq9KnbxsaNqxR4vEr4bG0aBlCjRpVcHNzpE2b+vj6enA1Mg4AHx93Ro/uS7161fDwcKZmLX/69G3DqVPhFBQUluel3FX7FrV55+XudL9L4uHh6qC3/bv7DK0aVsPfx62cIy1ZswbVGTGwI22a1Sl2zM7Wii8/eJEnW4ZSxced2tX9eG14Dy5eiSXheppe2cuRcaxct5dJRpBA7rmUzP7wZKJTcohOyeH7HVfI0RQQ4nOrFcjd3oI3O1dn6ppz5JfwtxTi68DKwzGci80gNi2XRXsjyczNp4bSvjwvBYBRY5+mSfMQlF5uePt60P/5zqSlZhATnQAUtabs3n6M9p2bEVo/GKW3GwNe6IImL5/jR84DoPR2Y+ioXtQOrYqbuxPValShS89WnDtzxSCvpZ4v9KJmg1q4eLjipnTjqT4dyEzP4Hpsol65TFUmu//ZScenO2JiWvyjoE7jELwDfHBwdsDd24OmTzUnMz2TjLSM8roUnaYNqvPigA48UcJrCeCp1vVoWLcqXp4uBPh58sqQLmTnqLlyNV5Xpl/3lgzo00avlaWiuHn35IfdKqpKm6isWrWK0NBQrK2tcXV1pUOHDmRlZXH48GE6duyIm5sbjo6OtG3blmPHjumde+nSJdq0aYOVlRW1a9dm82bjy86rVfPl5InLpKZmoNVquXDhKgkJqdSuE3TXc3Jy1FhZWWBawpuWsbueomLLvrMM6tnc0KH8Z1nZuSgUCuxsrXT7ctUaps1ewWsjeuLiXP4f5PdiooAOtT2wMjfl9LV0oKh1+YNedfjlQJReC8vtTkWn06G2Bw5WZigoqsPcTMGxq2nlFvvd5OYWtQDZ2BT9DlKS08lQZVG9pr+ujJm5GUHVfLl6Jfae9RjLa0lz45osrW/9XWkLtWz9czP1WzbExcP1vnXkafK4cPw8Ds4O2DnYPbJYy0Jefj7/bD2CrY0VVf2Vhg5HlIFK2fUTFxfHwIED+fzzz+nbty8ZGRns3r0brVZLRkYGQ4cOZe7cuQB89dVXdOvWjUuXLmFvb09hYSH9+vXDzc2NAwcOoFKpGD9+vGEvqAQDBnZg6ZKNvD3pG0xMTTBRKHhhSBeCg31LLJ+ZmcP6dfto06Z++QZaRn7/5zB2NlZ0a2sc3T6lpdHk8cMvm2jfui62Nrc+UL5e9A91alShdZNaBoxOX1V3W34Y1ggLMxNyNAW8s+o0kUlFXUEvtPSnoFDLitvGpNzpf6vPMK1vCP++2Yb8gkJy8wp5Z9VprqXllNcllEir1bL2jx0EVvVB6V3UKpehKkq27O7oGrG3tyE1RVViPVmZOWzZcIDmreqWeLw8abVa9v67B68qXrh63kpIju05isLEhLrN7x3jmUOn2bd5H/maPJzcnOk5pDemZqaPOuz/5MDRMD6ZswK1Jg8XJzs+mzwUR4fiXVoVUSUfolJ5E5X8/Hz69euHv3/RN6XQ0FAAnnrqKb2y33//Pc7OzuzcuZMePXqwZcsWzp8/T2RkJL6+RR/606dPp2vXrvd8TrVajVp9q79epSr5Ta6sbNt6hCtXYhn76tO4ujpw8WI0v/6yGUdHO2rXDtArm5OjZt7clXh5u9KjZ6tHGtejsnzdAfp1boSVpfn9CxuZ/PwCPpq9Aq1Wy/iRPXX79x4+z/EzESz4vPiYFUO6mpzN0B8PY2dlxpM13Hm/Zy3GLDuGpZkp/Zv4Mmzh4Xue/3LbIOytzBj3y3HSsvNoU8ONT/qFMHrJMcKvl9wKUx5Wr9hKXGwSY94YUOzYnc3m2pJ2Ark5an76bjWeXq507Nbi0QRaCrvX7yI5IZm+w5/W7UuMTeTUwVP0f7n/fcc9Bdetjm9VP7Izsjix7zibVmyk74inMTM3vo+OenUC+e7zMaSrstmw7QjTZv/O3E9extnRuFuAHkglz1SM76+tHNSrV4/27dsTGhpK586d6dSpE8888wzOzs4kJibywQcfsG3bNhISEigoKCA7O5uoqCgAzp8/T5UqVXRJCkCLFvd/Q5oxYwYffvjhI7um22k0eaxevYvRY/pRt25VAHx9PYiJTmTzpkN6iUpurpo5c1ZgaWXBmDH9MDPSb0v3cuBEOJejEvn+42GGDqXU8vML+HDmcuISU5k5Zbhea8rxM1eITUihx7BP9M6Z8uVvhNbyZ/aHI8s7XADyC7XEpBa1flyIy6CWtwPPNfEjMikLZ1sLVo9rqStrZmLCuA7BPNfUj35f78fHyZpnm/gx6PuDuq6hy4mZ1Pdz4unGvny+Icwg17RmxVbOnQ5nzPgBON3WxWZ/4xt5hioLh9s+8DIzsrG3t9GrIzdXw4/f/IGFpTlDR/XG1NSwr6Xd63cSERZB3+H9sLst9rirseRkZbNk1mLdPm2hln3/7uXUgZO88MZQ3X5LK0ssrSxxcnXC01fJwk8XEHHhCsGh1TE21lYW+Chd8VG6Uru6H0Nfn8XGbUcZ2LetoUMTD6lSJiqmpqZs3ryZffv2sWnTJubNm8fkyZM5ePAgY8eO5fr168yePRt/f38sLS1p0aIFGo0GKGpKvdODzMZ49913mTBhgu6xSqXCz+/RDOoqKCikoKCw2Bc+hYmCwtviz8lRM2f2CszMTBk79mnMjfBb0oP4dd0B6tb0o06wj6FDKZWbSUpMfDKzpozA8Y4PvkF92tC9fWO9fcPfnMeYYd1o2ajkQdSGoADMTU3YcCaew5GpesdmD6zPhtPxrD9ZNIjbyrxozEbhHa+jgkKtQQb7abVa1qzcxpmTl3nl9f64uDnqHXdxdcTewZaLF67i4+cJFP3erlyOoVvvJ3TlcnPULPj6D8zMTHnx5T4GfS1ptVp2/7OLiPNX6P1iXxyc9ae716hXA98g/feedUv/pnq9GtRscP8uxoL8gjKN95HRQl5FifU+ZNZPJaVQKGjVqhUffvghx48fx8LCgtWrV7N7925ee+01unXrRp06dbC0tCQpKUl3Xu3atYmKiiI29tZAuv3799/3+SwtLXFwcNDbHkZuroboqASio4pmJyQlpRMdlUBysgpra0uqV/fjj1U7CAuLIul6Gvv2nubA/rO6tVZyc9XMnvU7anUeQ4Z2JTdXTXp6JunpmRQWGsesn6xsNWcuxnDmYtF4h6i4ZM5cjCEm/taaHRlZuazddoLBRjiINidHzeWIOC5HFH1IxyWmcjkijoTraRQUFDDlq98Iu3KNya89S2FhISmpGaSkZpCXlw+Ai7M9gVU89TYATzdHvG5b56M8vdIuiHp+jigdrajqbsvL7YJo4O/Mv2fjUeXkc+V6lt6WX1BISqaaqJSiMSyRydlEp2Tzdrea1Pa2x8fJmoHN/Gga5MKusKT7PHvZW71iK8cOn2fQsG5YWlmgUmWhUmWRp8kDit4nnniyIds2HeL0yUvExybx+9KNWJib0aBx0Yd6bq6GBV//gUaTx7ODO5Obq9HVY4jX0q71O7l4KowOz3TCwsKc7IwssjOyyL/xd2VlY42rp6veZmJqgo2djW6tlfSUdI7uOkJibCIZaRnER8exaeVGTM1MqRLsf6+nfyRyctVcjozj8o1Zi/GJaVyOjCMxKY2cXA0Lf9vMuYvRJFxP49KVWL76bg3XU1S0aX5rllBKWgaXI+O4Fp8MQERUApcj41BlZpf79ZSWIWb97Nq1i549e+Lt7Y1CoWDNmjV6x7VaLVOnTsXb2xtra2vatWvH2bNn9cqo1WrGjRuHm5sbtra29OrVi5iYu49fu5uK+RX6IR08eJCtW7fSqVMnPDw8OHjwINevX6dWrVpUq1aNpUuX0rhxY1QqFRMnTsTa+taiQB06dKBGjRoMGTKEr776CpVKxeTJk8v9Gq5ejeer29bmWLliGwAtWoTw4vDujHqpF6v/3MnCH9eSlZWLi6sDffo8Qdu29W+cn0DEjQ/Q/03+Qa/u6TNewe2Ob5aGcOJCFE+/Ol/3eMrcNQD079aUuf8bDMCazcdAq6Vvx0aGCPGewq5c442pt9Z/+WbxBgA6t23AsP5Pse/IBQBGTfxa77xZU4dT/x6zswzJxdaCKb1q42pnSaY6n/DETN5YfoLDEan3P5milpMJy08y5qmqfPFsPawtTIlJzebjv8+zPzz5EUdf3P7dJwH4bs4Kvf39n+9Mk+YhALTr0IQ8TT6rf99KTnYuVQK8GPXqM1hZWQBwLer/7d15XE35/wfw103LvXXrpqtSSqs2JZElUZbSMLKOZZhRJNvYha+xxdiylMlvBmNGYZjGMBgmZKxhWqSIUqRolImkW9H++f3RdGauQlHdS+/n4+Exc875nM99f+459953n8/nnPM3HvzzAxqw8gepehavnAQtcdN+lm7F3gQAHA05LLW+79B+deoxAQBFxRbIfpCNG1HXUVJcAoGaKvSN9DF80idQFaq+uYIGlpqWBb9V/36Wtu+p+iy5uzpgziRPZD58jNMX4iH5Z0jO0qwNgvx9YPxPLxgAHD8di70Hz3HL8/yrjpXftGHw6N2piVry/igqKoK9vT0mTJiAESNG1Ni+YcMGBAYGIjQ0FBYWFli9ejXc3d2RkpICdfWq4dM5c+bg2LFjCAsLg1gsxvz58zFo0CDExcXVa2iUx2oby/jAJScnY+7cubh27RokEgmMjIwwc+ZMzJgxA/Hx8Zg8eTISExPRtm1brF27Fn5+fpgzZw53dU9qaip8fHwQExMDY2NjBAcH46OPPqrXnWklEglEIhF2nLsFgVC+Ljt9F4Pb68s6hAaX/LDp7xvRmKbtu/bmQu8ZHzcTWYfQ4NJza94s7303zEr3zYXeI0WFEnzU2Rj5+fnv3Etem+rfiUs3/4JQ/d3qLyyQoKetwVvF+vKd1xlj0NfXx5w5c7Bo0SIAVb0nurq6CAgIwJQpU5Cfnw9tbW3s3bsXo0ePBgBkZWXB0NAQ4eHh8PDwqPPrN8seFWtra5w8ebLWbQ4ODoiNlb5q4ZNPPpFatrCwQGRkpNS6ZpjvEUIIaQoNeNXPy1ecqqioQEWlfo96SE9Px6NHj9C/f3+pelxdXXHlyhVMmTIFcXFxKCsrkyqjr68PW1tbXLlypV6JSrOdo0IIIYQ0N4aGhhCJRNy/devW1buOR4+q7virqyvdQ6arq8tte/ToEZSVldGyZctXlqmrZtmjQgghhLwvGvKqn8zMTKmhn/r2pkjV+dIMXcbYG6+CrUuZl1GPCiGEECLHGvKqn5evPn2bRKV166pHE7zcM5KTk8P1srRu3RqlpaXIy8t7ZZm6okSFEEIIkWO8BvrXUExMTNC6dWup59yVlpbiwoUL6NGj6oaPnTt3hpKSklSZ7Oxs3Lx5kytTVzT0QwghhBAphYWFuHv3Lrecnp6OhIQEaGlpoW3btpgzZw7Wrl2Ldu3aoV27dli7di1UVVUxduxYAIBIJIKPjw/mz58PsVgMLS0t+Pn5wc7ODm5ubvWKhRIVQgghRJ7J4Fk/V69eRZ8+fbjl6jure3l5ITQ0FAsXLsSLFy8wffp05OXloVu3boiIiODuoQIAQUFBUFRUxKhRo/DixQv069cPoaGh9X68BCUqhBBCiByTxS30e/fu/drbbvB4PPj7+8Pf3/+VZfh8PrZu3YqtW7fW67VfRnNUCCGEECK3qEeFEEIIkWNv86ye2up4X1GiQgghhMgxGUxRkSs09EMIIYQQuUU9KoQQQog8a+ZdKpSoEEIIIXJMFlf9yBMa+iGEEEKI3KIeFUIIIUSO0VU/hBBCCJFbzXyKCiUqhBBCiFxr5pkKzVEhhBBCiNyiHhVCCCFEjjX3q34oUSGEEELkWQNMpn2P8xRKVGTNupUGhOoasg6jwSi8z1PLX6GVhoqsQ2hQH3VpI+sQGlxHbU1Zh9Dg1JULZB1Cg2uppiTrEBqUUuWH1R55RYkKIYQQIsea+VxaSlQIIYQQudbMMxW66ocQQgghcot6VAghhBA5Rlf9EEIIIURuNfdb6NPQDyGEEELkFvWoEEIIIXKsmc+lpUSFEEIIkWvNPFOhRIUQQgiRY819Mi3NUSGEEEKI3KIeFUIIIUSO8dAAV/00SCSyQYkKIYQQIsea+RQVGvohhBBCiPyiHhVCCCFEjjX3G75RokIIIYTIteY9+ENDP4QQQgiRW9SjQgghhMgxGvohhBBCiNxq3gM/NPRDCCGEEDlGPSrvqfhb6dh/OBIpaQ/xJK8A6/73GVy723DbGWP4IewMfouIhaToBdq3M8T8KYNh2la3Rl2MMcz/ajeirqXWqEeW/oy/i2/2ncH1lEz8/USC0PWTMNC1A7d9w/fhOHL6GrJynkFJqQU6WBriy6mD0Lm9seyCfg2P8WuR9XdejfWjPZ2wdMZw/HEpEb+ERyHpzl94JnmOX76dAyuzNjKI9NViLlxF2q00PH2cB0UlRei1bY2eHs7Q0m7JlfnzTDRSb6SiIL8QLVq0gE4bbfRwd4KeYWuuzC/f/4qH6Q+l6rawa4eBYz5qsrZUu5GUgV+OXUJqehae5hXA3+9TOHf59zOw55ezOH8lEY9z86Go2ALtTPQxYYwbrNsZcmV+/yMWZy/fwN30bDx/UYLDu76EUE3Q5G2pdic1ExERMXhw/xHy84swddowdHRox20vLi7F4V8v4HrCHRQVFUMs1kCfvp3h2tsBAPDkST6Wfrmj1rp9Jw9GZ0erJmnH6xQ9L8H2HyNw7s9byMsvhKWpPuZP9kR7i6rjwhjDd/v/wOFTMSgofIH2FoZYNG0ozIxqfgfKOxr6IQAAf39/HDlyBAkJCbIOpU6Ki0thbtIaH/frhC8D9tfY/uPhiwj77TKWzhoBQ/1WCP3lHOas2IWfvp0HNYGKVNmfj12Wy27B58WlaN+uDcYM6o6Ji3+osd3MUAfr5o+EURsxikvKsCPsHEbN/hbRvyxDq5bqMoj49X4KnoXKykpu+U7GI0xevBMevewBAC+KS9HRxhj9e3WA/5aDsgrztR6mP0SH7h3Quo0OKisrceV0FA6HHsX42eOgpKwEAGjZShN9PF0h0hKhvKwc1y4n4HDIUXjPHw/V//x42zq2h5NbN25ZUUk2X0fFJaUwNWqN/r0dsCowrMZ2Az0xZkwYBD3dligpLcOh3//E/9bsxu7gudDUUAMAlJSUoYt9O3Sxb4cffjrd1E2ooaSkDAYGOujRww47th+psf2XA2eRmvIAE3wGQSwWITkpHT/tPw2RphAdO7aDlpY6AjZOl9rnUuR1RJyKQXtb0yZqxeut3noIafcfYdX8UdDW0kD4uXhMX/o9fvl2HnRaibD70AXsP3IJK+aORFv9Vvjh57P4Ytn3OLTdD2qqKm9+ATnS3J/1Q4nKP/z8/DBz5kxZh1FnTp0t4dTZstZtjDEcOHYFXiN7o7eTLQBg2eyRGOS1FqcvJmCox78/DnfSsxF29DJ+2DQdnhPWNUnsddXPyQb9nF7duzPCw1FqedXsYdh3LApJd7Pg0qX290aWtDSFUss//HwOhnpiOHao+uL3dOsMAHj46GmTx1ZXw7yHSC27j3DDd2u/x98Pc2BgUtX7Y2Uv/d67DOyFW3FJePLoCdqa/dsLoaisCDV1tcYP+g26Oligq4PFK7f37WkvtTx1/Ec4eS4O9+4/Qic7MwDA8I97AACu30pvvEDrwdbOFLZ2r04o0u9lobuTLSwt2wIAerl0ROTF63hw/xE6dmwHBQUFiETS52tC/B10drQCn6/cqLHXRXFJGc5evonNy8aj0z+J05Rx7rgQlYSDJ6Iw7bP++OnoZUwY3Qd9e1R9B66cNwr9P1uNkxcSMGJAt9dVL3+a+SSVD2aOSmlp6VvtxxhDeXk5hEIhxGJxA0clG1l/5yE3rwBdO/7b1auspIiOtiZIvP2AW1dcUooVm8Mwb7InxHLYA1EfpWXl2HPkCjSEArRvJ1/DJbUpKyvH8bPXMMyjC3jvcZ9saXEJAICvyq91e0V5BW7G3oQyXxnarVtJbUtJSMH2NTux5+t9uHjiEkpL3u4z3JTKyssRfuYq1FT5MDNq/eYd5JSZeRvcuH4XeXkFYIwh5fZ9/P33U9jYmNRa/v79R8jMzIFzzw61bm9qFRWVqKishPJLvXAqykpIuJWBh38/RW5eAbo7SH8HdrI1wY3k+00dLnlHMk1UDh48CDs7OwgEAojFYri5uaGoqAi9e/fGnDlzpMoOHToU3t7e3LKxsTFWr14Nb29viEQi+Pr6IiMjAzweD2FhYejRowf4fD7at2+P8+fPc/udP38ePB4Pp06dgqOjI1RUVBAZGQl/f3907NhRqlzXrl2hpqYGTU1NODs74/79f0/wY8eOoXPnzuDz+TA1NcXKlStRXl7eSO9U/Tx9VgCg5l/wWiIhcvMKueWvf/gddlZGcOkmH3NS3kbEpZsw7usHQ9f52BF2Hr98PR3il9otj85cuYWCwmIM6e/45sJyijGGi+GXoG+kh1a60kn+vdvp+Gbldmz1/xbXLidg+IShEPxn2MfK3gIDRnvgk0nD0a1PF9y9lYbj+8Kbugl1FhWXAs/xX+Hjz1bh0O9XELDECyIN2fcGva3RY9ygpy/G4kXb8MW0zdgafBCfju0P83YGtZa/fOkGWuuJYSYnc6bUVFXQwaotvg87g8e5ElRUVCL8XDxupmbiSV4B9z0n1pT+A0ysqY7cvAJZhPxOeA30730ls6Gf7OxsfPrpp9iwYQOGDRuGgoICREZGgjFW5zo2btyIZcuWYenSpVLrFyxYgC1btsDGxgaBgYEYPHgw0tPTpXpMFi5ciE2bNsHU1BSampq4cOECt628vBxDhw6Fr68vfvrpJ5SWliImJob7y/fUqVP47LPPEBwcjF69eiEtLQ2TJ08GAKxYsaLWWEtKSlBSUsItSySSOrfzbb18YjIwbkJVZEwy4hLvITRwRqPH0ZicO7fD2d2L8DS/ED8e/RO+S0Nw4vv50NaS7x6iw6di0LOLJXTEIlmH8tbOHbuAx4+eYNTkT2psMzQ1wLgZY/CiqBg3r95CeNhJjJk6EqpCVQCAXRdbrmwrXTE0xZr46dufkfMwBzptdJqsDXVl394E2zdMR77kOU6cvYrVW35G8JopaCmS/6S4NmfPxiH9XhamfzEcWmIN3En9Cz/tj4BIpAZrG2OpsqWlZYiNScbAj51kE+wrrJo/Gqu+PogBXmvRQkEBlmb6+MjVHrfTsrgyL3dWMsbeyx7M5j6ZVmY9KtnZ2SgvL8fw4cNhbGwMOzs7TJ8+HUJh3T/4ffv2hZ+fH8zNzWFubs6tnzFjBkaMGAFra2ts27YNIpEIP/wgPRlz1apVcHd3h5mZWY0hH4lEgvz8fAwaNAhmZmawtraGl5cX2ratGs9ds2YN/ve//8HLywumpqZwd3fHV199hR07ap8lDwDr1q2DSCTi/hkaGr6y7LvS+ueviNxnhVLr8/KLuF6WuBtpePjoKTzGfYVew5ei1/CqZG/Jhn34YsnORoutoakJVGBqqA1HWxNsWTIWLVq0wP5jf8o6rNfK+jsPUfF3MPyjrrIO5a2dO3YB926n4xOfYVCv5cdaSVkJmmJN6LVtDffh/aCgwMPNuKRX1qejrw2FFgrIy33WiFG/PQFfGW1ai2FjYYj5U4dBoYUCTp6Nk3VYb6W0tAxHD1/EJyP7ooO9OQwMdNCnbyc4drHC6dOxNcpfi0tFaWkZujvZ1lKb7BjoifHd+imIPLgKv4f+D3uCZqC8ohL6ui0hbll1Tj55qffkaX5hjZ5mIv9k1qNib2+Pfv36wc7ODh4eHujfvz8++eQTtGzZ8s07/8PRsfZucyenfzN/RUVFODo6Ijk5uU77AoCWlha8vb3h4eEBd3d3uLm5YdSoUdDT0wMAxMXFITY2FmvWrOH2qaioQHFxMZ4/fw5VVdUadS5evBjz5s3jliUSSaMlK1UfVHXEJtyFpak+gKo5EQk30zHdywMA8PkIV3i6S78Hn88OxqyJH6NnF9lfevi2GGMoKZOPIbhXORIRCy1NIVy6Wcs6lHpjjOH8sQu4m3QPn0waDpFW3XqEGKuar/IquTlPUVlRKReTa+uEAWWvaY88q6ioREVFZY2eBQWeAlhlzR7ty5dvoIO9OdTVa36vyQMBXxkCvjIkhc/x57VUzJowAG10tSBuqY7o+LvcJf5lZeW4djMdM70HyDji+qOrfmSkRYsWOH36NK5cuYKIiAhs3boVS5YsQXR0NBQUFGoMAZWVldWoQ02t7l9qL38o37RvSEgIZs2ahZMnT+Lnn3/G0qVLcfr0aXTv3h2VlZVYuXIlhg8fXmM/Pr/2SYUqKipQUWm4S+KevyjBX9m53HJ2zlOk3suChroqWmtrYpRnD+w5eB6G+mIY6Imx5+B58FWU4O7SEQAgbqle6wRa3Vaa0NfVarA430Xh8xKk//WYW36QlYvE1L/QUkMVLUVq2BIaAY9ettAVi5AnKULIoUhkP36GwX0dZBj161VWVuJIRCwGuzlCsUULqW35kufIfpyHnNyqYcGMzKq2t2qpjlZaGk0ea23O/XYBt2+kYPBng6CsooSigiIAgApfBYpKiigrLUPM+ViYWplCTV0Vxc+LcT06EYWSQljYVvV6PsvNx+3rKTCxMAJfTYCnOU9x8cQlaOtpQ99Ir8nb9KK4ROpKq0c5z3A3IxsaQgHUharYf/gCnDpbQdxSHZKC5/gtIgaPn0rg0r09t8/TZwV4+qwQDx9VfSbTH/wNgUAFOq1E0BA2/Q98cXEpHj/+9549T548Q2bm31BTFUBLrIF2Fob49dB5KCkrQizWQGpqJqKibuGTkX2k6snJycPdO5mYMbPm8J6s/RmXCgYGozbayMzORfCucBi10cZgN0fweDx8OsQZIb+cQ1t9MQz1WyHkl3PgqyjhI9eOsg69/pr5VT8yvTyZx+PB2dkZzs7OWL58OYyMjHD48GFoa2sjOzubK1dRUYGbN2+iT58+r6ntX1FRUXBxcQFQNd8kLi4OM2bUfy6Gg4MDHBwcsHjxYjg5OWH//v3o3r07OnXqhJSUFKnhpqZ2++5DzFj2PbccvKtqIuLAPp2wdPYn+GyYC0pKyrBpx28oKHwBGwsDBPlPqHEPFXl2/fYDDPtiK7e8PPgwAGD0wK7YuHA07tz/Gz+Hx+BpfiFaitTgYN0Wv22bDSvTpv+xq6uo+DvIznmGYR5damw7F3ULyzYf4JYXrNsHAJj2mTumf96/yWJ8nRsxiQCAg9//KrXefYQb2neyBo/Hw9PHeUi6Fo7i5y/AVxVAt40ORvqOgPifCbctWiggMy0TCVeuo6y0FEKROkwsjdG9b1coKDT9aHRqWhb8Vu3ilrfvOVHVJlcHzJnkicyHj3H6QjwkBc+hrq4KS7M2CPL3gbHhvzcOO346FnsPnuOW5/lXDTX7TRsGj96dmqgl/7p//xGCNv97T5iDv1TF1t3JFt4TBmKSryeOHL6IXT8cx/OiYmhpaWDI0F5weelH/MrlRGhqqsP6FVcDyVLh82L83+6TyHmSDw11VfTtYYsvxntAUbHqDwCvEa4oKSnD+m1HUVD4AraWhvi/VT7v3T1UCMBj9Zm92oCio6Nx5swZ9O/fHzo6OoiOjsZnn32GI0eO4MGDB5g3bx4OHDgAMzMzBAUFISwsDMOGDUNoaCiAqqt+5syZI3V1UEZGBkxMTNC2bVts2bIF1tbWCAoKwv79+5Geno5WrVrh/Pnz6NOnD/Ly8qCpqcnt+98bvqWnp+O7777D4MGDoa+vj5SUFHz66adYvXo1pk2bhlOnTmHQoEFYsmQJRo4cCQUFBdy4cQOJiYlYvXp1ndovkUggEolwMTETQnX5+Gu5IZjrfnjjv39LSt5c6D3yw9UHby70nvnYXP4m4L6rtPz37+qUN3HUl4/e2oZSWCBBd+s2yM/Ph4ZGw3+PV/9O3HuYC/V3rL9AIoFpG3GjxdqYZNajoqGhgYsXL2LLli2QSCQwMjLC5s2bMWDAAJSVleH69esYP348FBUVMXfu3Dr3pgDA+vXrERAQgPj4eJiZmeHo0aNo1arVm3f8h6qqKm7fvo3du3cjNzcXenp6mDFjBqZMmQIA8PDwwPHjx7Fq1Sps2LABSkpKsLKywqRJk+r9PhBCCCGv09yv+pFZj0pjqO5RiY+Pl7onijyiHpX3B/WoyD/qUXk/UI9K/VT/TqRnNUyPiok+9agQQgghpMG9+1U/7/NsWkpUCCGEEDnW3Id+PqhExdjYuF53tiWEEEKIfPtgHkpICCGEkA/PB9WjQgghhHxoaOiHEEIIIXKrud9Cn4Z+CCGEECK3qEeFEEIIkWM09EMIIYQQudXMn0lIQz+EEEIIkV/Uo0IIIYTIs2bepUKJCiGEECLH6KofQgghhBA5RT0qhBBCiByjq34IIYQQIrea+RQVSlQIIYQQudbMMxWao0IIIYQQuUU9KoQQQogca+5X/VCiQgghhMgxmkxLZIIxBgAoKiyQcSQNSyKolHUIDa6goETWITSokueFsg6hwRUV8mUdQoN7UfjhHafCgg/rJ6f6+7v6+7yxSCQSuahDVj6ss+Y9UlBQdYIPcLKRcSSEvP+CZB0AadYKCgogEokavF5lZWW0bt0a7UwMG6S+1q1bQ1lZuUHqako81tipIKlVZWUlsrKyoK6uDl4j9slJJBIYGhoiMzMTGhoajfY6TYna9H6gNsm/D609QNO2iTGGgoIC6OvrQ0Ghca5NKS4uRmlpaYPUpaysDD7//et9pB4VGVFQUICBgUGTvZ6GhsYH80VUjdr0fqA2yb8PrT1A07WpMXpS/ovP57+XyUVDosuTCSGEECK3KFEhhBBCiNyiROUDp6KighUrVkBFRUXWoTQYatP7gdok/z609gAfZpuaO5pMSwghhBC5RT0qhBBCCJFblKgQQgghRG5RokIIIYQQuUWJCmlSvXv3xpw5cwAAxsbG2LJli0zjaSyMMUyePBlaWlrg8XhISEiQdUhv9N9jQ5rW+/BZ4PF4OHLkiKzDaDD+/v7o2LGjrMMgdUCJCpGZ2NhYTJ48WdZhAAAyMjIaNKE4efIkQkNDcfz4cWRnZ8PW1rZB6iXygZK695+fnx/OnDkj6zBIHdCdaYmUsrIyKCkpNclraWtrN8nryEJaWhr09PTQo0ePRnuN0tLS9/K5Hc0FYwwVFRVQVKSv2cbwtud/9XERCoUQCoWNEBlpaNSjIsdOnjyJnj17QlNTE2KxGIMGDUJaWhqAf3sAfv31V/Tp0weqqqqwt7fHn3/+KVXHzp07YWhoCFVVVQwbNgyBgYHQ1NTktld3f+7atQumpqZQUVHB7t27IRaLUVIi/dTgESNGYPz48XWOv6ioCOPHj4dQKISenh42b94stf3l7m5/f3+0bdsWKioq0NfXx6xZs7ht2dnZ+PjjjyEQCGBiYoL9+/dL7V9bj8izZ8/A4/Fw/vx5AEBeXh7GjRsHbW1tCAQCtGvXDiEhIQAAExMTAICDgwN4PB569+5d53a+zNvbGzNnzsSDBw/A4/FgbGwMxhg2bNgAU1NTCAQC2Nvb4+DBg9w+FRUV8PHxgYmJCQQCASwtLfH111/XqHfo0KFYt24d9PX1YWFh8dYxvkplZSUWLlwILS0ttG7dGv7+/ty2wMBA2NnZQU1NDYaGhpg+fToK//OE39DQUGhqauLIkSOwsLAAn8+Hu7s7MjMzuTLV59uOHTu483LkyJF49uwZAODixYtQUlLCo0ePpOKaP38+XFxc6tSG3r17Y9asWa9sR35+PiZPngwdHR1oaGigb9++uH79Ore9+n3+rzlz5nDnhLe3Ny5cuICvv/4aPB4PPB4PGRkZOH/+PHg8Hk6dOgVHR0eoqKggMjISaWlpGDJkCHR1dSEUCtGlSxf88ccfdWrLuzh48CDs7OwgEAggFovh5uaGoqIixMbGwt3dHa1atYJIJIKrqyuuXbsmte+dO3fg4uICPp8PGxsbnD59ulFjqq2HaujQofD29uaWjY2NsXr1anh7e0MkEsHX15f73IeFhaFHjx7g8/lo374995kH8Mrj8vLQz/nz59G1a1eoqalBU1MTzs7OuH//Prf92LFj6Ny5M/h8PkxNTbFy5UqUl5c3yPtC3oARuXXw4EF26NAhlpqayuLj45mnpyezs7NjFRUVLD09nQFgVlZW7Pjx4ywlJYV98sknzMjIiJWVlTHGGLt06RJTUFBgGzduZCkpKeybb75hWlpaTCQSca+xYsUKpqamxjw8PNi1a9fY9evX2fPnz5lIJGIHDhzgyj1+/JgpKyuzs2fP1jn+adOmMQMDAxYREcFu3LjBBg0axIRCIZs9ezZjjDEjIyMWFBTEGGPsl19+YRoaGiw8PJzdv3+fRUdHs++++46ry83NjXXs2JFFRUWxuLg45urqygQCAbd/9fsRHx/P7ZOXl8cAsHPnzjHGGPviiy9Yx44dWWxsLEtPT2enT59mv/32G2OMsZiYGAaA/fHHHyw7O5vl5ubWuZ0ve/bsGVu1ahUzMDBg2dnZLCcnh3355ZfMysqKnTx5kqWlpbGQkBCmoqLCzp8/zxhjrLS0lC1fvpzFxMSwe/fusR9//JGpqqqyn3/+mavXy8uLCYVC9vnnn7ObN2+yxMTEt46xNq6urkxDQ4P5+/uz1NRUtnv3bsbj8VhERARjjLGgoCB29uxZdu/ePXbmzBlmaWnJpk2bxu0fEhLClJSUmKOjI7ty5Qq7evUq69q1K+vRowdXpvp869u3L4uPj2cXLlxg5ubmbOzYsVwZCwsLtmHDBm65rKyM6ejosF27dr1zOyorK5mzszPz9PRksbGxLDU1lc2fP5+JxWLumHt5ebEhQ4ZI1Tl79mzm6urKGKs6vk5OTszX15dlZ2ez7OxsVl5ezs6dO8cAsA4dOrCIiAh29+5d9uTJE5aQkMC2b9/Obty4wVJTU9mSJUsYn89n9+/f5+r/72ehIWRlZTFFRUUWGBjI0tPT2Y0bN9g333zDCgoK2JkzZ9jevXtZUlISS0pKYj4+PkxXV5dJJBLGGGMVFRXM1taW9e7dmztGDg4ODAA7fPhwo8Tk6urKfS9UGzJkCPPy8uKWjYyMmIaGBtu4cSO7c+cOu3PnDve5NzAwYAcPHmRJSUls0qRJTF1dnT158oQxxl55XFasWMHs7e0ZY1XnmEgkYn5+fuzu3bssKSmJhYaGcsfo5MmTTENDg4WGhrK0tDQWERHBjI2Nmb+//1u/H6TuKFF5j+Tk5DAALDExkfuAfv/999z2W7duMQAsOTmZMcbY6NGj2ccffyxVx7hx42okKkpKSiwnJ0eq3LRp09iAAQO45S1btjBTU1NWWVlZp1gLCgqYsrIyCwsL49bl5uYygUBQa6KyefNmZmFhwUpLS2vUlZyczACw2NhYbt2dO3cYgHolKp6enmzChAm1xlvb/u8iKCiIGRkZMcYYKywsZHw+n125ckWqjI+PD/v0009fWcf06dPZiBEjuGUvLy+mq6vLSkpKGiTGl7m6urKePXtKrevSpQtbtGhRreUPHDjAxGIxtxwSEsIAsKioKG5d9bGLjo5mjFWdby1atGCZmZlcmRMnTjAFBQWWnZ3NGGMsICCAWVtbc9uPHDnChEIhKywsfOd2nDlzhmloaLDi4mKp7WZmZmzHjh2MsTcnKtWv8fIPa/UP4pEjR94Yo42NDdu6dSu33NCJSlxcHAPAMjIy3li2vLycqaurs2PHjjHGGDt16lStx+hdE5XXxVTXRGXo0KFSZao/t+vXr+fWlZWVMQMDAxYQEMAYe/Vx+W+ikpubywBwfzi8rFevXmzt2rVS6/bu3cv09PRe22bSMGjoR46lpaVh7NixMDU1hYaGBjc88eDBA65Mhw4duP/X09MDAOTk5AAAUlJS0LVrV6k6X14GACMjoxrzRXx9fREREYGHDx8CAEJCQuDt7Q0ej1fn2EtLS+Hk5MSt09LSgqWlZa3lR44ciRcvXsDU1BS+vr44fPgw162akpICRUVFdOrUiStvbm6Oli1b1imWatOmTUNYWBg6duyIhQsX4sqVK/Xa/20lJSWhuLgY7u7u3Li4UCjEnj17uKE8ANi+fTscHR2hra0NoVCInTt3Sh1rALCzs2vUeSn/PZ+AqnOq+nw6d+4c3N3d0aZNG6irq2P8+PHIzc1FUVERV15RURGOjo7cspWVFTQ1NZGcnMyta9u2rdSTw52cnFBZWYmUlBQAVUMrd+/eRVRUFABg165dGDVqFNTU1N65HXFxcSgsLIRYLJY6Funp6VLH4l38t/1A1RDowoULYWNjA01NTQiFQty+fbvGsW1I9vb26NevH+zs7DBy5Ejs3LkTeXl5AKq+H6ZOnQoLCwuIRCKIRCIUFhZy8SQnJ9d6jBozprp6+b2tLb7qc/C/59zr9gWqvpu8vb3h4eEBT09PfP3118jOzua2x8XFYdWqVVLnjK+vL7Kzs/H8+fN6tYHUHyUqcszT0xO5ubnYuXMnoqOjER0dDaBqElm1/058rU4iKisrAVRNGns5sWC1PDGhth8ABwcH2NvbY8+ePbh27RoSExOlxovfpLbXeR1DQ0OkpKTgm2++gUAgwPTp0+Hi4oKysrJX1vXf9QoKCjXWlZWVSZUfMGAA7t+/jzlz5iArKwv9+vWDn59fveJ8G9XH4/fff0dCQgL3LykpiZuncuDAAcydOxcTJ05EREQEEhISMGHCBKljDdR+rBrSyxOpeTweKisrcf/+fQwcOBC2trY4dOgQ4uLi8M033wCo+T7Xlsy+LsGt3lb9Xx0dHXh6eiIkJAQ5OTkIDw/HxIkTG6QdlZWV0NPTkzoOCQkJSElJwYIFCwBUnUsvn3Mvt/F1Xj5GCxYswKFDh7BmzRpERkYiISEBdnZ2NY5tQ2rRogVOnz6NEydOwMbGBlu3boWlpSXS09Ph7e2NuLg4bNmyBVeuXEFCQgLEYjEXT22ft7r+gfK2MdX1Pa/P+f9yzG/aNyQkBH/++Sd69OiBn3/+GRYWFlyyXFlZiZUrV0qdM4mJibhz5w74fH6dYyJvh6ajy6nc3FwkJydjx44d6NWrFwDg0qVL9arDysoKMTExUuuuXr1a5/0nTZqEoKAgPHz4EG5ubjA0NKzzvubm5lBSUkJUVBTatm0LoGoya2pqKlxdXWvdRyAQYPDgwRg8eDC++OILWFlZITExEVZWVigvL0d8fDw6d+4MALh79y43ARP49wqi7OxsODg4AECtlxpra2vD29sb3t7e6NWrFxYsWIBNmzZxvRQVFRV1bmNd2djYQEVFBQ8ePHhl2yMjI9GjRw9Mnz6dW9dQf+E3hKtXr6K8vBybN2/mksIDBw7UKFdeXo6rV69yPXcpKSl49uwZrKysuDIPHjxAVlYW9PX1AQB//vknFBQUpCYHT5o0CWPGjIGBgQHMzMzg7OzcIO3o1KkTHj16BEVFRRgbG9daRltbGzdv3pRal5CQIJX8KCsr1/lciYyMhLe3N4YNGwYAKCwsREZGxlvFXx88Hg/Ozs5wdnbG8uXLYWRkhMOHDyMyMhLffvstBg4cCADIzMzEkydPuP1sbGxqPUaNGZO2trZUD0ZFRQVu3ryJPn361KneqKgobrJ1eXk54uLiMGPGjHrH5+DgAAcHByxevBhOTk7Yv38/unfvjk6dOiElJQXm5ub1rpO8O0pU5FTLli0hFovx3XffQU9PDw8ePMD//ve/etUxc+ZMuLi4IDAwEJ6enjh79ixOnDhR57+Oxo0bBz8/P+zcuRN79uyp12sLhUL4+PhgwYIFEIvF0NXVxZIlS7gfuZeFhoaioqIC3bp1g6qqKvbu3QuBQAAjIyPu6oDJkydj27ZtUFJSwvz58yEQCLi2CAQCdO/eHevXr4exsTGePHmCpUuXSr3G8uXL0blzZ7Rv3x4lJSU4fvw4rK2tAVT9FS8QCHDy5EkYGBiAz+dDJBLVq82voq6uDj8/P8ydOxeVlZXo2bMnJBIJrly5AqFQCC8vL5ibm2PPnj04deoUTExMsHfvXsTGxnLDfbJmZmaG8vJybN26FZ6enrh8+TK2b99eo5ySkhJmzpyJ4OBgKCkpYcaMGejevbvUkCOfz4eXlxc2bdoEiUSCWbNmYdSoUWjdujVXxsPDAyKRCKtXr8aqVasarB1ubm5wcnLC0KFDERAQAEtLS2RlZSE8PBxDhw6Fo6Mj+vbti40bN2LPnj1wcnLCjz/+iJs3b3IJMFB1BUp0dDQyMjIgFAqhpaX1ytc0NzfHr7/+Ck9PT/B4PCxbtozrZWss0dHROHPmDPr37w8dHR1ER0fj8ePHsLa2hrm5Ofbu3QtHR0dIJBIsWLAAAoFA6j2ytLTE+PHjsXnzZkgkEixZsqRRY1JTU8O8efPw+++/w8zMDEFBQVJ/iLzJN998g3bt2sHa2hpBQUHIy8urVy9ceno6vvvuOwwePBj6+vpISUlBamoqd5Xj8uXLMWjQIBgaGmLkyJFQUFDAjRs3kJiYiNWrV9f3rSD1REM/ckpBQQFhYWGIi4uDra0t5s6di40bN9arDmdnZ2zfvh2BgYGwt7fHyZMnMXfu3Dp3VWpoaGDEiBEQCoU1Ltesi40bN8LFxQWDBw+Gm5sbevbsyfWIvExTUxM7d+6Es7MzOnTogDNnzuDYsWMQi8UAgD179kBXVxcuLi4YNmwYfH19oa6uLtWWXbt2oaysDI6Ojpg9e3aNLxBlZWUsXrwYHTp0gIuLC1q0aIGwsDAAVePawcHB2LFjB/T19TFkyJB6t/d1vvrqKyxfvhzr1q2DtbU1PDw8cOzYMS4RmTp1KoYPH47Ro0ejW7duyM3NlepdkbWOHTsiMDAQAQEBsLW1xb59+7Bu3boa5VRVVbFo0SKMHTsWTk5OEAgE3HtczdzcHMOHD8fAgQPRv39/2Nra4ttvv5Uqo6CgAG9vb1RUVNTrkvg34fF4CA8Ph4uLCyZOnAgLCwuMGTMGGRkZ0NXVBVCVJC1btgwLFy5Ely5dUFBQUCMGPz8/tGjRAjY2NtDW1n7tfJOgoCC0bNkSPXr0gKenJzw8PKTmWzUGDQ0NXLx4EQMHDoSFhQWWLl2KzZs3Y8CAAdi1axfy8vLg4OCAzz//HLNmzYKOjg63r4KCAg4fPoySkhJ07doVkyZNwpo1axo1pokTJ8LLywvjx4+Hq6srTExM6tybAgDr169HQEAA7O3tERkZiaNHj6JVq1Z13l9VVRW3b9/GiBEjYGFhgcmTJ2PGjBmYMmUKgKpz4vjx4zh9+jS6dOmC7t27IzAwEEZGRvV+H0j98Vh9JxOQ95qvry9u376NyMjIOpV3d3eHtbU1goODGzmy+vnrr79gaGiIP/74A/369ZN1OARVvWJz5sx57V/C/v7+OHLkSJ3uAOzr64u///4bv/32W8MFST4oGRkZMDExQXx8PN0O/wNGQz8fuE2bNsHd3R1qamo4ceIEdu/eXeOv19o8ffoUEREROHv2LP7v//6vCSJ9vbNnz6KwsBB2dnbIzs7GwoULYWxsXOebgJH3R35+PmJjY7Fv3z4cPXpU1uEQQmSMEpUPXExMDDZs2ICCggKYmpoiODgYkyZNeuN+nTp1Ql5eHjeOL2tlZWX48ssvce/ePairq6NHjx7Yt29fk93unzSdIUOGICYmBlOmTIG7u7uswyGEyBgN/RBCCCFEbtFkWkIIIYTILUpUCCGEECK3KFEhhBBCiNyiRIUQQgghcosSFUKaMX9/f6n7T3h7e7/Vzf3eVUZGBng83mvvr2JsbIwtW7bUuc7Q0FBoamq+c2w8Hg9Hjhx553oIIW+HEhVC5Ez1U6p5PB6UlJRgamoKPz8/qacUN5avv/4aoaGhdSpbl+SCEELeFd1HhRA59NFHHyEkJARlZWWIjIzEpEmTUFRUhG3bttUoW1ZW1mD3k2mo5xsRQkhDoR4VQuSQiooKWrduDUNDQ4wdOxbjxo3jhh+qh2t27doFU1NTqKiogDGG/Px8TJ48GTo6OtDQ0EDfvn1x/fp1qXrXr18PXV1dqKurw8fHB8XFxVLbXx76qaysREBAAMzNzaGiooK2bdtyz32pfk6Rg4MDeDweevfuze0XEhICa2tr8Pl8WFlZ1bgbckxMDBwcHMDn8+Ho6Ij4+Ph6v0eBgYGws7ODmpoaDA0NMX36dBQWFtYod+TIEVhYWIDP58Pd3R2ZmZlS248dO4bOnTuDz+fD1NQUK1euRHl5eb3jIYQ0DkpUCHkPCAQClJWVcct3797FgQMHcOjQIW7o5eOPP8ajR48QHh6OuLg4dOrUCf369cPTp08BAAcOHMCKFSuwZs0aXL16FXp6em98nMLixYsREBCAZcuWISkpCfv37+ce3hcTEwMA+OOPP5CdnY1ff/0VALBz504sWbIEa9asQXJyMtauXYtly5Zh9+7dAICioiIMGjQIlpaWiIuLg7+/P/z8/Or9nigoKCA4OBg3b97E7t27cfbsWSxcuFCqzPPnz7FmzRrs3r0bly9fhkQiwZgxY7jtp06dwmeffYZZs2YhKSkJO3bsQGhoaIM8hI8Q0kAYIUSueHl5sSFDhnDL0dHRTCwWs1GjRjHGGFuxYgVTUlJiOTk5XJkzZ84wDQ0NVlxcLFWXmZkZ27FjB2OMMScnJzZ16lSp7d26dWP29va1vrZEImEqKips586dtcaZnp7OALD4+Hip9YaGhmz//v1S67766ivm5OTEGGNsx44dTEtLixUVFXHbt23bVmtd/2VkZMSCgoJeuf3AgQNMLBZzyyEhIQwAi4qK4tYlJyczACw6OpoxxlivXr3Y2rVrperZu3cv09PT45YBsMOHD7/ydQkhjYvmqBAih44fPw6hUIjy8nKUlZVhyJAh2Lp1K7fdyMgI2tra3HJcXBwKCwshFoul6nnx4gXS0tIAAMnJyZg6darUdicnJ5w7d67WGJKTk1FSUlKvp1M/fvwYmZmZ8PHxga+vL7e+vLycm/+SnJwMe3t7qKqqSsVRX+fOncPatWuRlJQEiUSC8vJyFBcXo6ioCGpqagAARUVFODo6cvtYWVlBU1MTycnJ6Nq1K+Li4hAbGyvVg1JRUYHi4mI8f/5cKkZCiGxQokKIHOrTpw+2bdsGJSUl6Ovr15gsW/1DXK2yshJ6eno4f/58jbre9hJdgUBQ730qKysBVA3/dOvWTWpbixYtAACsAR4vdv/+fQwcOBBTp07FV199BS0tLVy6dAk+Pj5SQ2RA1eXFL6teV1lZiZUrV2L48OE1yvD5/HeOkxDy7ihRIUQOqampwdzcvM7lO3XqhEePHkFRURHGxsa1lrG2tkZUVBTGjx/PrYuKinplne3atYNAIMCZM2dqfeK2srIygKoeiGq6urpo06YN7t27h3HjxtVar42NDfbu3YsXL15wydDr4qjN1atXUV5ejs2bN0NBoWqq3YEDB2qUKy8vx9WrV9G1a1cAQEpKCp49ewYrKysAVe9bSkpKvd5rQkjTokSFkA+Am5sbnJycMHToUAQEBMDS0hJZWVkIDw/H0KFD4ejoiNmzZ8PLywuOjo7o2bMn9u3bh1u3bsHU1LTWOvl8PhYtWoSFCxdCWVkZzs7OePz4MW7dugUfHx/o6OhAIBDg5MmTMDAwAJ/Ph0gkgr+/P2bNmgUNDQ0MGDAAJSUluHr1KvLy8jBv3jyMHTsWS5YsgY+PD5YuXYqMjAxs2rSpXu01MzNDeXk5tm7dCk9PT1y+fBnbt2+vUU5JSQkzZ85EcHAwlJSUMGPGDHTv3p1LXJYvX45BgwbB0NAQI0eOhIKCAm7cuIHExESsXr26/geCENLg6KofQj4APB4P4eHhcHFxwcSJE2FhYYExY8YgIyODu0pn9OjRWL58ORYtWoTOnTvj/v37mDZt2mvrXbZsGebPn4/ly5fD2toao0ePRk5ODoCq+R/BwcHYsWMH9PX1MWTIEADApEmT8P333yM0NBR2dnZwdXVFaGgodzmzUCjEsWPHkJSUBAcHByxZsgQBAQH1am/Hjh0RGBiIgIAA2NraYt++fVi3bl2Ncqqqqli0aBHGjh0LJycnCAQChIWFcds9PDxw/PhxnD59Gl26dEH37t0RGBgIIyOjesVDCGk8PNYQA8aEEEIIIY2AelQIIYQQIrcoUSGEEEKI3KJEhRBCCCFyixIVQgghhMgtSlQIIYQQIrcoUSGEEEKI3KJEhRBCCCFyixIVQgghhMgtSlQIIYQQIrcoUSGEEEKI3KJEhRBCCCFyixIVQgghhMit/wcuqtl+8zc6yQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Confusion Matrix\n",
+    "cm = confusion_matrix(y_true, y_pred)\n",
+    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=class_labels)\n",
+    "disp.plot(cmap=plt.cm.Blues)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "22c9d061",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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bbzw9JCGEEEIIIUQrJUEpIYRoRbZv387o0aOJjIzkqaeeYvLkyZ4ekhBCCCGEEKKVkvI9IYQQQgghhBBCCNHs9J4egBBCCCGEEEIIIYRofSQoJYQQQgghhBBCCCGanQSlhBBCCCGEEEIIIUSza3WNzo1GI6dPnyY4OBidTufp4QghhBCihVMUhcLCQtq0aYNe33rv58k5lBBCCCFc5er5U6sLSp0+fZrExERPD0MIIYQQ55lTp07Rtm1bTw/DY+QcSgghhBDuquv8qdUFpYKDgwH1BxMSEuLh0QghhBCipSsoKCAxMdF8DuFp8+fP5+uvv+bgwYP4+/szbNgwXnzxRbp16+Z0u1WrVjFnzhz27dtHmzZtePjhh5k5c6bL7yvnUEIIIYRwlavnT60uKKWlm4eEhMgJlRBCCCFc1lJK1latWsU999zDoEGDqKqqYu7cuYwbN479+/cTGBhod5uUlBSuvPJK7rjjDj7++GPWrVvH3XffTXR0NNddd51L7yvnUEIIIYRwV13nT60uKCWEEEIIcT77+eefbb7/8MMPiYmJYdu2bVx66aV2t/n3v/9Nu3btWLBgAQDdu3dn69atvPLKKy4HpYQQQgghGlvr7dYphBBCCHEByM/PByAiIsLhOhs2bGDcuHE2y8aPH8/WrVuprKy0u015eTkFBQU2DyGEEEKIxiRBKSGEEEKI85SiKMyZM4cRI0bQq1cvh+tlZmYSGxtrsyw2NpaqqipycnLsbjN//nxCQ0PND2lyLoQQQojGJuV7QgghhBDnqXvvvZfdu3ezdu3aOtet2dNBURS7yzWPPfYYc+bMMX+vNSwVQghxfjEajVRUVHh6GOIC4+3tjcFgaPB+JCglhBBCCHEemjVrFt9//z2rV692OtUyQFxcHJmZmTbLsrKy8PLyIjIy0u42vr6++Pr6Ntp4hRBCNL+KigpSUlIwGo2eHoq4AIWFhREXF9egyWAkKCWEEEIIcR5RFIVZs2bxzTffsHLlSjp06FDnNkOHDuWHH36wWfbrr78ycOBAvL29m2qoQgghPEhRFDIyMjAYDCQmJqLXS/ce0TgURaGkpISsrCwA4uPj670vCUoJIYQQQpxH7rnnHv73v//x3XffERwcbM6ACg0Nxd/fH1BL79LT01m8eDEAM2fO5M0332TOnDnccccdbNiwgQ8++IBPP/3UY59DCCFE06qqqqKkpIQ2bdoQEBDg6eGIC4x2zpGVlUVMTEy9S/k8Hip9++236dChA35+fgwYMIA1a9Y4Xf+TTz6hb9++BAQEEB8fz2233UZubm4zjVYIIYQQwrPeeecd8vPzGTVqFPHx8ebHkiVLzOtkZGSQmppq/r5Dhw4sW7aMlStX0q9fP/7xj3/wz3/+k+uuu84TH0EIIUQzqK6uBsDHx8fDIxEXKi3Y6WgmX1d4NFNqyZIlzJ49m7fffpvhw4fzn//8hwkTJrB//37atWtXa/21a9cydepUXn/9dSZNmkR6ejozZ87k9ttv55tvvvHAJxBCCCGEaF5ag3JnFi1aVGvZyJEj2b59exOMSAghREvWkH4/QjjTGL9bHs2Ueu2115gxYwa333473bt3Z8GCBSQmJvLOO+/YXX/jxo0kJSVx33330aFDB0aMGMFdd93F1q1bm3nkQgghhBBCCCGEEKIhPBaUqqioYNu2bYwbN85m+bhx41i/fr3dbYYNG0ZaWhrLli1DURTOnDnDl19+yVVXXeXwfcrLyykoKLB5CCGEOE8oCvz0CKx51dMjEUII5/LT4LObIcV5KwohhBDNb9SoUcyePdvTwxB2eCwolZOTQ3V1NbGxsTbLY2Nja01ZrBk2bBiffPIJU6ZMwcfHh7i4OMLCwvjXv/7l8H3mz59PaGio+ZGYmNion0MIIUQTyjsJm/4NfzwL1VWeHo0QQji271s4+CNs/o+nRyKEEOctnU7n9DFt2rR67ffrr7/mH//4R4PGNm3aNCZPntygfYjaPN7ovGYNoqIoDusS9+/fz3333ceTTz7Jtm3b+Pnnn0lJSWHmzJkO9//YY4+Rn59vfpw6dapRxy+EEKIJFanTzKIYoSTHs2MRQghnikw3VYvlWCWEEPWVkZFhfixYsICQkBCbZW+88YbN+q422I6IiCA4OLgphiwayGNBqaioKAwGQ62sqKysrFrZU5r58+czfPhwHnroIfr06cP48eN5++23WbhwIRkZGXa38fX1JSQkxOYhhBDiPFGcbXledMZz4xBCiLoUmY5XJTIrtBBC1FdcXJz5ERoaik6nM39fVlZGWFgYn3/+OaNGjcLPz4+PP/6Y3NxcbrzxRtq2bUtAQAC9e/fm008/tdlvzfK9pKQknn/+eaZPn05wcDDt2rXj3XffbdDYV61axeDBg/H19SU+Pp5HH32UqipLpv+XX35J79698ff3JzIykjFjxlBcXAzAypUrGTx4MIGBgYSFhTF8+HBOnjzZoPGcLzwWlPLx8WHAgAEsX77cZvny5csZNmyY3W1KSkrQ622HbDAYANdmohFCCHGe0TKlwHLBJ4QQLZEWOJdMKSFEC6UoCiUVVR55NOb1+iOPPMJ9993HgQMHGD9+PGVlZQwYMIAff/yRvXv3cuedd3LLLbewadMmp/t59dVXGThwIDt27ODuu+/mr3/9KwcPHqzXmNLT07nyyisZNGgQu3bt4p133uGDDz7g2WefBdQMsBtvvJHp06dz4MABVq5cybXXXouiKFRVVTF58mRGjhzJ7t272bBhA3feeWermTXRy5NvPmfOHG655RYGDhzI0KFDeffdd0lNTTWX4z322GOkp6ezePFiACZNmsQdd9zBO++8w/jx48nIyGD27NkMHjyYNm3aePKjCCGEaAqSKSWEOF9ox6vSc2CsBr3Bs+MRQogaSiur6fHkLx557/3zxhPg0zjhh9mzZ3PttdfaLHvwwQfNz2fNmsXPP//MF198wZAhQxzu58orr+Tuu+8G1EDX66+/zsqVK0lOTnZ7TG+//TaJiYm8+eab6HQ6kpOTOX36NI888ghPPvkkGRkZVFVVce2119K+fXsAevfuDcDZs2fJz89n4sSJdOrUCYDu3bu7PYbzlUeDUlOmTCE3N5d58+aRkZFBr169WLZsmfkfKSMjg9TUVPP606ZNo7CwkDfffJMHHniAsLAwLrvsMl588UVPfQQhhBBNSYJSQojzhTmzU1EDU4FRHh2OEEJcqAYOHGjzfXV1NS+88AJLliwhPT2d8vJyysvLCQwMdLqfPn36mJ9rZYJZWVlOtnDswIEDDB061Ca7afjw4RQVFZGWlkbfvn25/PLL6d27N+PHj2fcuHFcf/31hIeHExERwbRp0xg/fjxjx45lzJgx3HDDDcTHx9drLOcbjwalAO6++25zdLKmRYsW1Vo2a9YsZs2a1cSjEkII0SJYl+8VS/meEKKFMlbbTsZQkitBKSFEi+PvbWD/vPEee+/GUjPY9Oqrr/L666+zYMECevfuTWBgILNnz6aiosLpfry9vW2+1+l0GI3Geo3J3oRtWsmiTqfDYDCwfPly1q9fz6+//sq//vUv5s6dy6ZNm+jQoQMffvgh9913Hz///DNLlizh8ccfZ/ny5Vx88cX1Gs/5xOOz7wkhhBAOWfdmkUwpIURLVZKrzhJq/b0QQrQwOp2OAB8vjzyasj/SmjVruPrqq/nLX/5C37596dixI0eOHGmy97OnR48erF+/3qZ31vr16wkODiYhIQFQf/7Dhw/nmWeeYceOHfj4+PDNN9+Y1+/fvz+PPfYY69evp1evXvzvf/9r1s/gKR7PlBJCCCEcKrZudF6/dGohhGhyNYPm0uxcCCGaTefOnfnqq69Yv3494eHhvPbaa2RmZjZJX6b8/Hx27txpsywiIoK7776bBQsWMGvWLO69914OHTrEU089xZw5c9Dr9WzatInff/+dcePGERMTw6ZNm8jOzqZ79+6kpKTw7rvv8qc//Yk2bdpw6NAhDh8+zNSpUxt9/C2RBKWEEEK0XDY9pSQoJYRooWoenyRTSgghms0TTzxBSkoK48ePJyAggDvvvJPJkyeTn5/f6O+1cuVK+vfvb7Ps1ltvZdGiRSxbtoyHHnqIvn37EhERwYwZM3j88ccBCAkJYfXq1SxYsICCggLat2/Pq6++yoQJEzhz5gwHDx7kv//9L7m5ucTHx3Pvvfdy1113Nfr4WyKd0phzM54HCgoKCA0NJT8/n5CQEE8PRwghhCPVlfAPq54sfmHw6EmPDUe0XnLuoJKfgxO7PoNvrC4eLnscLn3Ic+MRQgigrKyMlJQUOnTogJ+fn6eHIy5Azn7HXD1vkJ5SQgghWqaajc3L8qCq3CNDEUIIp2qW75Wc9cw4hBBCiPOMBKWEEEK0TFpQKjAa9N62y4QQoiXRyvf0ps4Y0lNKCCGEcIkEpYQQQrRMRaYAVFCsGpgC6SslhGiZtIB5VFf1q/SUEkIIIVwiQSkhhBAtk3WmVFCM+lyCUkKIlkgr34tOVr+WSKaUEEII4QoJSgkhhGiZik0BKJug1BnH6wshhKdomZ0xPdSv0lNKCCGEcImXpwcghBBC2KVlSgXFgJePaZlkSgkhWiDt2BTTXf0q5XtCCCGESyQoJYQQomUqsirf8/I1LZOglBCihamusjQ214JSlSVQUQI+AZ4blxBCCHEekPI9IYQQLZN1+V6glO8JIVqoklxAAZ0ewtqDwcdquRBCCCGckaCUEEKIlsm6fM/cUyrbc+MRQgh7tAB6QCQYvCAgSv1emp0LIYQQdZKglBBCiJbJXL4XJY3OhRAtl3ZcCopVvwZEql8lU0oIITxm1KhRzJ492/x9UlISCxYscLqNTqfj22+/bfB7N9Z+WgsJSgkhhGh5jEZLlkFgjOVir1gypYQQLYx1/zuAQFNQqliCUkII4a5JkyYxZswYu69t2LABnU7H9u3b3d7vli1buPPOOxs6PBtPP/00/fr1q7U8IyODCRMmNOp71bRo0SLCwsKa9D2aiwSlhBBCtDxleWCsUp8HRlsypcoL1ObBQgjRUpgzpUzHKcmUEkKIepsxYwZ//PEHJ0+erPXawoUL6devHxdddJHb+42OjiYgoHkmn4iLi8PX17dZ3utCIEEpIYQQLY+WEeUXCl4+4BsCBtMf92KZgU8I0YJY978D6SklhBANMHHiRGJiYli0aJHN8pKSEpYsWcKMGTPIzc3lxhtvpG3btgQEBNC7d28+/fRTp/utWb535MgRLr30Uvz8/OjRowfLly+vtc0jjzxC165dCQgIoGPHjjzxxBNUVlYCaqbSM888w65du9DpdOh0OvOYa5bv7dmzh8suuwx/f38iIyO58847KSoqMr8+bdo0Jk+ezCuvvEJ8fDyRkZHcc8895veqj9TUVK6++mqCgoIICQnhhhtu4MwZSxuMXbt2MXr0aIKDgwkJCWHAgAFs3boVgJMnTzJp0iTCw8MJDAykZ8+eLFu2rN5jqYtXk+1ZCCGEqK8ibeY900WeTqeW8OWnqqUy4UkeG5oQQtioebySTCkhREulKFDpoYxz7wD1fK4OXl5eTJ06lUWLFvHkk0+iM23zxRdfUFFRwc0330xJSQkDBgzgkUceISQkhKVLl3LLLbfQsWNHhgwZUud7GI1Grr32WqKioti4cSMFBQU2/ac0wcHBLFq0iDZt2rBnzx7uuOMOgoODefjhh5kyZQp79+7l559/5rfffgMgNDS01j5KSkq44ooruPjii9myZQtZWVncfvvt3HvvvTaBtxUrVhAfH8+KFSs4evQoU6ZMoV+/ftxxxx11fp6aFEVh8uTJBAYGsmrVKqqqqrj77ruZMmUKK1euBODmm2+mf//+vPPOOxgMBnbu3Im3tzcA99xzDxUVFaxevZrAwED2799PUFCQ2+NwlQSlhBBCtDzFNXq0AARFm4JSLaPZudGoMHvJTny89Lx8fR/zSZMQopWpVb4XoX6VoJQQoqWpLIHn23jmvf9+GnwCXVp1+vTpvPzyy6xcuZLRo0cDaunetddeS3h4OOHh4Tz44IPm9WfNmsXPP//MF1984VJQ6rfffuPAgQOcOHGCtm3bAvD888/X6gP1+OOPm58nJSXxwAMPsGTJEh5++GH8/f0JCgrCy8uLuLg4h+/1ySefUFpayuLFiwkMVD//m2++yaRJk3jxxReJjVX7poaHh/Pmm29iMBhITk7mqquu4vfff69XUOq3335j9+7dpKSkkJiYCMBHH31Ez5492bJlC4MGDSI1NZWHHnqI5ORkALp06WLePjU1leuuu47evXsD0LFjR7fH4A4p3xNCCNHymMthrINSWrPzllG+dzyniO93nebLbWmknSv19HCEEJ5Ss3wv0FS+J43OhRCiXpKTkxk2bBgLFy4E4NixY6xZs4bp06cDUF1dzXPPPUefPn2IjIwkKCiIX3/9ldTUVJf2f+DAAdq1a2cOSAEMHTq01npffvklI0aMIC4ujqCgIJ544gmX38P6vfr27WsOSAEMHz4co9HIoUOHzMt69uyJwWAwfx8fH09WVv3OeQ8cOEBiYqI5IAXQo0cPwsLCOHDgAABz5szh9ttvZ8yYMbzwwgscO3bMvO59993Hs88+y/Dhw3nqqafYvXt3vcbhKsmUEkII0fKYy2Gsg1Ixtq952N70AvPzHafySIxonuaZQogWRsr3hBDnC+8ANWPJU+/thhkzZnDvvffy1ltv8eGHH9K+fXsuv/xyAF599VVef/11FixYQO/evQkMDGT27NlUVFS4tG9FUWotq5nxvnHjRv785z/zzDPPMH78eEJDQ/nss8949dVX3fociqI4zKa3Xq6Vzlm/ZjQa3Xqvut7TevnTTz/NTTfdxNKlS/npp5946qmn+Oyzz7jmmmu4/fbbGT9+PEuXLuXXX39l/vz5vPrqq8yaNate46mLZEoJIYRoeczlezGWZdrzFlK+tyc93/x816k8zw1ECOE51VWW4JOWzSmNzoUQLZVOp5bQeeLhZpuDG264AYPBwP/+9z/++9//ctttt5kDKmvWrOHqq6/mL3/5C3379qVjx44cOXLE5X336NGD1NRUTp+2BOg2bNhgs866deto3749c+fOZeDAgXTp0qXWjIA+Pj5UV1fX+V47d+6kuLjYZt96vZ6uXbu6PGZ3aJ/v1KlT5mX79+8nPz+f7t27m5d17dqV+++/n19//ZVrr72WDz/80PxaYmIiM2fO5Ouvv+aBBx7gvffea5KxggSlhBBCtER2y/daWqaUJSi1U4JSQrROJTmAAjq9pZeUlilVeg6Mzi9WhBBC2BcUFMSUKVP4+9//zunTp5k2bZr5tc6dO7N8+XLWr1/PgQMHuOuuu8jMzHR532PGjKFbt25MnTqVXbt2sWbNGubOnWuzTufOnUlNTeWzzz7j2LFj/POf/+Sbb76xWScpKYmUlBR27txJTk4O5eXltd7r5ptvxs/Pj1tvvZW9e/eyYsUKZs2axS233GLuJ1Vf1dXV7Ny50+axf/9+xowZQ58+fbj55pvZvn07mzdvZurUqYwcOZKBAwdSWlrKvffey8qVKzl58iTr1q1jy5Yt5oDV7Nmz+eWXX0hJSWH79u388ccfNsGsxiZBKSGEEC2P3UbnLScoZTQq7D9tKd/bm55PZXX9UqyFEOcx7XgUEAV6Uy8QLTilGKE0zyPDEkKIC8GMGTM4d+4cY8aMoV27dublTzzxBBdddBHjx49n1KhRxMXFMXnyZJf3q9fr+eabbygvL2fw4MHcfvvtPPfcczbrXH311dx///3ce++99OvXj/Xr1/PEE0/YrHPddddxxRVXMHr0aKKjo/n0009rvVdAQAC//PILZ8+eZdCgQVx//fVcfvnlvPnmm+79MOwoKiqif//+No8rr7wSnU7Ht99+S3h4OJdeeiljxoyhY8eOLFmyBACDwUBubi5Tp06la9eu3HDDDUyYMIFnnnkGUINd99xzD927d+eKK66gW7duvP322w0eryM6xV5B5QWsoKCA0NBQ8vPzCQkJ8fRwhBBC2LOgD+SdhOm/QjvTLCqpG2HheAhPgr/t8ujwTuQUM+qVlfh46fHz0lNQVsWPs0bQK6H2VMDi/NcSzx1Wr17Nyy+/zLZt28jIyOCbb76p84T8k08+4aWXXuLIkSOEhoZyxRVX8MorrxAZGenSe7bEn4PHHfkNPrkOYnvDX9dalr/QDsry4Z4tEN005RlCCFGXsrIyUlJS6NChA35+fp4ejrgAOfsdc/W8QTKlhBBCtDzFpl4s2ixWYJsp5eH7KXtPq6V73eOC6ZsYBqjNzoVoLsXFxfTt29flO61r165l6tSpzJgxg3379vHFF1+wZcsWbr/99iYe6QVOmw3UutQYrJqdS18pIYQQwhmZfU8IIUTLUlEMlaZmkEF2Gp1XlkBFEfgGN//YTLQm570SQokI9GHNkRx2puZxy8XtPTYm0bpMmDCBCRMmuLz+xo0bSUpK4r777gOgQ4cO3HXXXbz00ktNNUS3VVYbKSitJDLI19NDcZ028YL1pAyglvOdPS4z8AkhhBB1kEwpIYQQLYvWo8XLD3yCLMt9g8A70HYdD9mXrvaT6pUQSj9TptSutDzPDUiIOgwbNoy0tDSWLVuGoiicOXOGL7/8kquuusrhNuXl5RQUFNg8msq6ozmMfmUlc7/Z22Tv0SSKtEkZagalTJlSxZIpJYQQQjgjQSkhhBAti7l0L6b29MFaiYwHg1KKopjL93q1CTWX7x3LLqKgrNJj4xLCmWHDhvHJJ58wZcoUfHx8iIuLIywsjH/9618Ot5k/fz6hoaHmR2JiYpONLybYl7RzpfyyP5OjWYVN9j6Nzly+VyMoFaiV70mmlBBCCOGMBKWEEEK0LI56tAAExdqu4wHpeaXklVTibdDRNS6IqCBf2ob7oyiwJy3fY+MSwpn9+/dz33338eSTT7Jt2zZ+/vlnUlJSmDlzpsNtHnvsMfLz882PU6dONdn4usQGM65HLIoC76w83mTv0+gclu9pQamzzTseIYQQ4jwjQSkhhBAtS7GpHCbQXlDKqtm5h+w19ZPqGhuMr5c6BbxWwrdTmp2LFmr+/PkMHz6chx56iD59+jB+/HjefvttFi5cSEZGht1tfH19CQkJsXk0pbtHdwbgu53ppOeVNul7NRqH5XumSRqk0bkQogVQPDxBjLhwGY3GBu9DGp0LIYRoWYqcBKW0bAQtO8ED9mr9pNqEmpf1Swzjx90Z7EjN89CohHCupKQELy/b0z6DQQ2qtpSLlX6JYQzrFMn6Y7m8t/o4T/+pp6eHVDdH5XsBUr4n7Di5AY78AqMeA6/zqKG/OG95e3uj0+nIzs4mOjoaXc22CELUk6IoVFRUkJ2djV6vx8fHp977kqCUEEKIlsVpppSpfM+TmVJaP6kES9aIdaaUoihy0ieaXFFREUePHjV/n5KSws6dO4mIiKBdu3Y89thjpKens3jxYgAmTZrEHXfcwTvvvMP48ePJyMhg9uzZDB48mDZt2njqY9Ryz+jOrD+Wy2dbUrn3ss5EteSZ+KorLUEn7dikCTRlSkmjc2Htt6fh1EZoOxiSr/T0aEQrYDAYaNu2LWlpaZw4ccLTwxEXoICAANq1a4deX/8iPAlKCSGEaFkcZR6AxxudK4piLt/rmWDJlOqVEIpBryOnqJzT+WUkhPl7ZHyi9di6dSujR482fz9nzhwAbr31VhYtWkRGRgapqanm16dNm0ZhYSFvvvkmDzzwAGFhYVx22WW8+OKLzT52Z4Z1iqRv21B2peXz4boUHhqf7OkhOaYFnHQG8I+wfU16Sgl7ijJNXz2X7Stan6CgILp06UJlpUzGIhqXwWDAy8urwTdjJSglhBCiZXFWvufhRudnCsrJKarAoNfRI96SKeXnbSA5Lph9pwvYmZonQSnR5EaNGuW07G7RokW1ls2aNYtZs2Y14agaTqfT8ddRnZn58TYWbzjJzJGdCPbz9vSw7NOOQ4FRUPMOcYApSCU9pYQ1LUhZKsFK0bwMBoO5ZFuIlkYanQshhGhZWnD5npYl1Tk6CD9v25M7rYRvV1peM49KiAvLuB6xdI4JorCsio83pta9gacUOcnq1BqdV5ZARUnzjUm0XNWVUK72JJQMOiGEsJCglBBCiJbFWfmeFqgqOgMeaM6s9ZPqmVB7FjJzXylpdi5Eg+j1OmaO7ATAB2tTKKus9vCIHNCCUoF2jlW+waA3ZXhJs3MBtoGo0nOeG4cQQrQwEpQSQgjRclRXWk7W7WZKmS7+qiugLL/5xmVib+Y9jRaU2pOeT1V1w6fHFaI1u7pfGxLC/MkpKueLrac8PRz7nAXQdTpLs3MJSgmwLdmT3wkhhDCToJQQQoiWw9w4WF+7cTCAtz/4mrKUPFDCp5Xv9UqoHZTqFB1EsK8XpZXVHD5T1NxDE+KC4m3Qc+elHQH4z+rjLTPQ66x8D6yanUsAQmD7eyDle0IIYSZBKSGEEC2H1k8qwE7jYI12AdjMzc6zC8vJLChDp4MebWqX7+n1OvokqsGqnafymnVsQlyIbhiYSGSgD2nnSvlh92lPD6c2Z+V7IEEpYcv690AanQshhJnHg1Jvv/02HTp0wM/PjwEDBrBmzRqH606bNg2dTlfr0bNnz2YcsRBCiCbjrBxGY2523rxTau8z9ZPqEBVIkK/9yWv7tg0DYOcp6RciREP5+xiYPqIDAO+sPIbR2Px95JzSjkHaMakmCUoJa9bZUZIpJYQQZh4NSi1ZsoTZs2czd+5cduzYwSWXXMKECRNITbU/08obb7xBRkaG+XHq1CkiIiL4v//7v2YeuRBCiCZRpM28F+V4HXOz8+bNlNp32nE/KY15Br5Tzd/vSogL0V8ubk+wrxeHzxTx+0HPzLrpkJbZGWSn/x1YjmNaWbJo3Wwypc6BsYU28BdCiGbm0aDUa6+9xowZM7j99tvp3r07CxYsIDExkXfeecfu+qGhocTFxZkfW7du5dy5c9x2223NPHIhhBBNQrvIc1QOA1aZUs17gbonTesnVbt0T6MFpQ5nFVJUXuXW/k/mFvPIl7tJySmu9xibSkpOMY99vYf1R1vWxXVWQRmfbDrJ1IWb+XSz/Rta4vwW6u/NX4a2B+CtFUdRPDDrpkNSvifcYZMdpXhksg4hhGiJPBaUqqioYNu2bYwbN85m+bhx41i/fr1L+/jggw8YM2YM7du3b4ohCuG+6kpPj0BUV0JLumg5X1RXNe7Prb7/F1wq3/NMptTe046bnGtiQvxoE+qHosDutDy39v/U9/tYsvUUcz7f2aIuvHOLyrnlg018ujmVm97fxD9+3E9Zpefu8B/PLuLfq45xzdvrGPz878z9Zi+rD2ezbE+Gx8Ykmtb04R3w9dKz81QeG463kABPdaWlL1Cd5XstK5grPKRmHykp4RNCCMCDQamcnByqq6uJjbX9Qx4bG0tmZmad22dkZPDTTz9x++23O12vvLycgoICm4cQTeK7e+GVLlDYvH1uhJWiLHilK3z7V0+P5PxSXgT/7AcfX9c4+9v8HjyfAMdWuL+tVubirHxPuwBsxkbneSUVpJ0rBaCnVr5XXQXvjoIPxoPRMjNYv3ZhgHslfIcyC1l5SM0S25Gax4+7W0aApayymjs/2kbauVJC/NQ+Wh+sTeHqN9dxIKOef08VBRZfDW8PhcoyF1ZX2J2Wx8u/HGTsa6u47NVVvPDTQXak5gFqdtrDV3Tj6T9Jf8nzXmkeZB+utTg62JcbBiYCam+pFkHL6tQZwD/c/jrmoJQEHwS1M+Ykg04IIQCw36m1Gel0OpvvFUWptcyeRYsWERYWxuTJk52uN3/+fJ555pmGDFEI1xxZrvYISN8GyVd6ejStU+pG9U7koWWeHsn5JWMX5J9SHxXF4BPYsP0dXArV5XDkV+g02r1t6yqHAY80Otf6SbWLCCDU31tdmH0QTu9Qn59LgchOgBokWbYn061m5++tOQ5AsK8XheVVvPDTQcb2iMXP29B4H8JNiqLw2Nd72HbyHMF+Xnx993C1xPCr3Rw6U8jVb67jofHdmDGiA3p93X+3zQpOw/GV6vOMndDuYrurZeaX8d6a4yzbk0FGviV45aXXMbRTJON6xjG2eyxxoX71/5Ci5Ti4DD67CdoOhNt/q/XynZd25H+bU1lzJIfdaXn0MU0q4DHmY1W045lCtaCU9JQSUDs4KTPwCSEE4MFMqaioKAwGQ62sqKysrFrZUzUpisLChQu55ZZb8PHxcbruY489Rn5+vvlx6tSpBo9diFqM1ZasjYJ0z46lNTuXon4ty5c70+7IscpMyDnSePvLOuD+tuaeUg4aB1u/1ozle3vT7fSTOrPP8jxzj/mpZQa+PJf2faagjO92qseN/0wdQFyIH+l5pSxaf6IhQ26wN/84yjc70jHodbxz8wA6xwRxefdYfp59KWO6x1BRbeS5ZQe4+f1NnM4rdX3HWiAPIGN3rZezCst45od9XPryCj5Ym0JGfhkBPgau7B3Hgin92PbEWD6aMYRbLm4vAakLSVwvQFF/Pypq91VLjAjg6r5tAHh7RQvIlipyodRYy/iUjBgBlt8Dn2DT93KeIoQQ4MGglI+PDwMGDGD58uU2y5cvX86wYcOcbrtq1SqOHj3KjBkz6nwfX19fQkJCbB5CNLribFBM5TuFLaPsplU6m2J5fi7F8XrClnUgqqFBqfJCS2A2+5D729c1mxVYle9l25TNNaU96Xb6SZ2xBKKsg1K924Zi0Os4U1BOZn7d5WkfrjtBZbXC4KQIhnWK4qHx3QB464+j5BaVN84HcNOPu0/z6nI1uDjv6p6M6GIpp4wK8uW9qQOZf21v/L0NbDieyxULVvP9rtOu7Txjp+V55i7z03PFFcz/6QCXvrSCD9edoKLKyOCkCN6bOpDtT4zl7ZsHMLl/giVTTVxYwtpBaCIYq+DUZrurzBylZiP+sj+To1lFzTm62lzpf6dlSpWebbZjlWjBtCCUKatWMqWEEELl0dn35syZw/vvv8/ChQs5cOAA999/P6mpqcycORNQs5ymTp1aa7sPPviAIUOG0KtXr+YeshD2WQeiCly8MBONzzoQdVaCUi6zyZSqRyDJZl9WQa3C02qPGFcZje5lShmr1JLZZqCV7/VqYxWUytxr9dyS8RPg40XXWPVOeF0lfEXlVXyy6SSglicBXNM/gV4JIRSWV7Hgt0bIXHPTzlN5PPC5GiyaMaIDNw+pPZmITqfjxsHtWPa3S+iXGEZBWRX3fbqDv322g/zSOprc18iUyi+t5LVfD3HJSyv4z6rjlFUa6ZcYxkczBrPkros9XsYomlF7003Jk+vsvtw1NpixPWJRFHh9ee3eU83KlVJjLSilGKEsr8mHJFqw6kooN/UZjOqifpUMOiGEADzcU2rKlCnk5uYyb948MjIy6NWrF8uWLTPPppeRkUFqqu0Uz/n5+Xz11Ve88cYbnhiyEPYVWpWhSlDKcyRTqn5sglINvNCrmWmVcxgSB7u2bVmeGmgC50EpLx+1sXDpOTVbITCyXkN1VWFZJSk5ajlRzzZ1l+8B9EsM5UBGATtO5XFFr3iH+16y5RSFZVV0jA7ksmT14lav1zH3yh7c+N5G/rc5lVuHtadzTHDjfSAn0vNKuf2/WymvMnJZcgx/v7K70/U7RAXy5cyhvLniKP/64yjf7TzNuqM59E4IpW14AG3D/UmMMH0NDyDM3wvd6Z3m7avPHGD0i79y1pRQ1rNNCHPGduWy5BiX+kuKC0z74bB7CZx0PAvz7DFd+P3AGZbuyeDPR7K5pIuTY0VTcqV8z+ANvqFqMKIkFwIimmdsouUx30DRQYR6A0LK94QQQuXxRud33303d999t93XFi1aVGtZaGgoJSUlTTwqIdwkQSnPq6pQG3Vrzp7w2FDOK5WlkGcV/Lcz85VbamZaZR90PSilZUn5hYKXr/N1g2LVk/yiMxDjPHBiLe1cCZ9sSmX68A5EB9fxHib7TVlSbUL9iAwybVOUZSrfMQVOCjOgKNtcdtgvMYxPN59il5O+UpXVRhauVYOnd1zS0aZZ+NBOkYztEcvy/Wd4ftlBFk4b5PJnrK+i8ipmLNpCTlE5yXHB/PPG/hhcaGDuZdAze0xXRnaN5v4lOzmRW8IK00yCNXX2Ocdv+hyqMFCGD0FKKbHlJ4mK7cmcsV0Z1yPOvabp4sLSfrj6NW2rOjOjd+2eYT3bhDJ1aBKL1p/giW/38vPsSz2TSedK+R6ogajyfLXZuZYhI1ofLSvKP9xy00XK94QQAvBw+Z4QF4yaQSlF8dxYWqv8U5a+XgDnTnhsKOeV3GOAok5rDpB7FKqr6r8/rY+Ud4D6Neug69u6UrqnqWez879/s5d3Vh7jsa/31L2yif1+UqbSvchOlrveVj2m+iWqU8TvScun2mj/eLBsTwbpeaVEBflwTf+EWq8/NiEZL72OPw5mseaI/SBPY6k2Kvzt0x0czCwkKsiXD6YNIsjXvftW/duF8/PsS/nk9iG8cG1v7h3dmav7tWFA+3BiTAHATlVHAThkbMteYxIAz11s5Ke/XcoVveIlINXaRXZSA87V5epMtg48MK4rMcG+nMgt4T+rjjfjAK2YM6WcT84jzc4FYMmKCohQA1MAJc1Tfi6EEC2dBKWEaAzWPaUqi6G8wHNjaa200j0tuCLle67JNZXbtekHXv5grIS8k/Xfn1a+12Ws+jXbjaCUKz1aNNqFoBtBqUOZhaw+rAZ3fjtwhvXHXJum3dxPKsFOP6nYXhDXW31uNZNc55ggAn0MFFdUcySrsNY+FUXhvTXqxfStQ5PsZnp0jA7ilqFqOftzSw84DG41hvnLDvD7wSx8vfS8N3UACWH+9dqPn7eB4Z2j+PPgdjw4vhtv/Lk/X/11GJvnjuHgP67ghaFqwNO33UXEdlUz6C7yOeVSRpZoBXQ6q75Sjkv4gv28eWJiDwDeWnmUEzm1Z+trcubjVR1BdK2vVIlrxxtxgdKCkgGRljJOCVQKIQQgQSkhGod1phRAgczA1+y0IFTbgerXgtNq+YdwTgsiRSdDVGf1eX1mzQO1ketZ01Tt3f/k/r6KTRdtgVHO1wOroNQZl3f/wVo1COTjpf7pe27pAYwuBHr2mjOlrPtJmYJScb0gvo/63KqvlEGvo3dbNYhlr4Rvw/Fc9qYX4O9t4C8X124krvnb5V0I8fPiYGYhX2475XC9hvjfplTeN5URvnpDX/q3C2+S9/HzNhCep/bh6tz3Ejr0Gqq+YBXME8JcwndyrdPVJvaJZ0TnKCqqjDz5/T6U5s5Qdrl8TzKlBJZSvYBI21kZhRBCSFBKiEZRVDMole6ZcbRmWqZU20HgEwQoDcv4aS20xuZRXSCqm+0yd507oTYq9w6ETpepywrSoMzFzEFXL/LA3LvJ1UyprMIyvt2h9nt766aLCPb1Yt/pAr7e4fz/aklFFcey1annbWbe05qcx/aCuNpBKbCU8O20E5R6d7UaILthYFvCA30cvn9YgA/3Xa72oXnl18MUlzegtNKOg5kFPPGdGmB7YGxXJvZp06j7t6EooDU5b9PPNphnNDraSrQ2WlDq1GY10O2ATqdj3tU98THoWX04m5/2Zjpct9FVVVgaV9dVvqdlxRRLUKpVM/eUilAfoJb0SbsHIYSQoJQQjULLlNLS+AslU6rZaZlS4UkQ3kF9flZK+OpkDkp1VR/Wy9ylZUVFdVYvxILi3Ntffcr3il0LSn204SQV1Ub6twtjbI9Y7r1MzQp7+ZeDlFQ4DvQcyCjAqEB0sC8xIaamy1UVls9qXb6XewQqLBNx9EtUg1g7UvNs9nkos5CVh7LR62D6iA51jn3q0CTaRwaQXVjOf1Ydc+nzuuqtFceoNiqM6R5j/pk0mbxUNTNA763+3KK6gsEXKgql3FZYRCerPXcqSyBjl9NVO0YHMXNUJwDm/bCfokYO2jqk9b/Te4FfmPN1paeUANueUlqg0lgJFUWeG5MQQrQQEpQSoqGqqywX020uUr/KDHzN76yp2W1EB4hIUp/Lha5zRqOlfC+qK0Q3MChlDnCZMq6iTV+zDri2vTvle1rgyoVMqdKKaj7eqGbN3XGJ2pT81mFJtA3350xBOe+tdvx7sjddzfLqbd1PKueQejHhFwqhbdUAWWC02mg/a795NS1T6vCZQpvA1/umXlJX9IqjfWRgneP38dLz2IRkAN5dc5yM/NI6t3HFydxilu5Wj1X3j+2KTtfEfZ0ydqpfY7qrsysavCFW7QtEppTwCRO93pItdcJ5CR/A3aM60S4igMyCMl5f3sDZQ11VbNVPSl/HqbS5p5QEpVo166CUdwB4mW5yyO+FEEJIUEqIBivOxjx7mVaOIkGp5mU0Wmbbi+hoyZSSGficKzytZiPovdQMMy2YlH24fiUF1llXoGY8gOvNzt0q33M9KPXV9jTOlVSSGOHP+J5q9paft4FHTYGef686xpkC+/3HzP2k2lj3k7Iq3dPp1Ie5hM8SXIkL9SMuxA+jos7CB3CmoIxvd6olg1qAzBXje8YxOCmCskojL/9Sz55fNby7+jhGBUZ1i6andWliUzm9Q/3apr9lmfZzk75SwpoLzc41ft4G5l3dE4BF60+w/3QzTDRS5MaxytxTShqdt2rWjc51OtsSPiGEaOUkKCVEQ2mlekGxataE9TLRPIoyoapMDQyGJqrZUiDle3XRgkgRHdWslchOoNNDeb5bDcTNtJI2LeMqJtl2eV20kpi6ZrMCS/leSQ4Yqx2uZjQqLDQ18Z4+vIPNLG9X9Y7nonZhlFZW8+qv9se413SB29Nm5j1T76jYXpZlWglfjb5SfU0lfFpfqUXrT1BZrTA4KcKthuI6nY7HJ3YH4Ovt6exOy3N5W3uyCsv4YlsaAH8d2alB+3KZuZ+UVVAqvnYwTwhzUCp1g9P/35pR3WK4sncc1UaFx7/d49IEBg3iTqmxZEoJsA1KgaWET5qdCyGEBKWEaDCtn1RwHASbmgRLo/PmpQWfwhLV4Io5U0qCUk5Zl+6BWlIVnmR6zc0yGEWx2p9WvudmplSRG0GpgEhAp5bMFTvOQPj9YBbHc4oJ9vPi/wYm2rymBnrU8rEvtqXVyrAoq6zmyJlCAHpZB6W0mfdie1qWaUGpGhk/WgnfrrQ8isqr+EQrI7zU9SwpTZ+2YVzTPwGAZ5ceaNBsYwvXnqCiyshF7cIY3CGi3vtxmaJYZUr1syyP66t+lUwpYS2uD/gEQ3mB5f9bHZ6c2JNAHwPbU/P4oolmqjQzZ3XW0eQcpNG5UGnBJy1Dyt90U6LknGfGI4QQLYgEpYRoqCKroFSIFpSSTKlmZW5ybgpGaYGVcydlVi9ntMBTpFWDay1A5Wp2k6YwQ21YrTOomVdgCUrln4LyQufbVxRDZbH63JWglMHL0nvKSbPz90z9m24a0o4gX69ar1/ULpyJfeJRFHhu2X6bQM+hzEKqjAoRgT60CfWzbKSV78VZZUrF97W8ZpXZ0S8xDICdqXks2XKKgrIqOkYHcnmyCxkWdjw0vhu+Xno2p5zlh931O84UlFWag2N3j+rc9L2kQC2lLcsDgw/E9LAsj+2pZucVZ1kC/ELoDdDuYvW5CyV8oJbL3j9WPX7N/+kgZ4srmmp0VuV7LhyrtONUZTFUNk4/OHEeqpUpJRl0QgihkaCUEA1VaCcoVZIDVeWeG1NrY93kHNQSPr0XVJerfZOEfTUzpayfa6+5vC+tFLADePmozwMiLOUtdWVeaaV7Xn7gG+zae5qbndsvNdydlsfmlLN46XVMG5bkcDePXJGMj0HPuqO5rDhkCXDtPa32gerZJsQSuCk8o45Vp4fo7padRHRUm9dWlUKuZYa83m1D0engdH4Z76w8Cqi9pPT6+gWC2oT5c/coNYj49Pf7yC1y/zjz0YaTFJZX0TU2iMvqGRxzm7nJeQ81I0/jEwCRXUzrSLaUsJLkerNzzbRhSSTHBZNXUskLP7k4wUJ9uFO+5xuizjgJEoBoraqroEz9eyLle0IIUZsEpYRoKK1/VHC8mo6tzagifaWaj1a+p2XoGLzUwJT1a6I2p0EpNzOlsms0OdeYZ+Cro4TPPPNejNoE1hXmZufZdl9+f436bz+pbxviQ/0d7iYxIoDbRiQB8NzSA1RWq9l12sx7tqV7pp5REZ3UgIpGb7CU81n1Rwry9aJrjBpkyymqICrIx1yCV19/HdWJ5LhgzhZX8MwP++vewEpZZTUfrksx76e+wTG32WtyrjH3ldrVPGMR5wdtBr6T612eeMHLoOe5a9QMxs+3prH1RBNd8GtBdFcanet0khXT2pVqJXo68A9Tn0qjcyGEMJOglBANVWjK0giKVU8+g+PV72UGvuZTs3wPLFlTMgOffeWFliyyKKvyPS2IVN9MqZpBqRhTNlFdfaXMmQdRrr+n1s/FTqZUel4pS/eogeEZIzrUer2me0Z3JiLQh2PZxXy2ORWwnnnPOihlp3RPY252XrOvVJj5+dShSfh5G+ocjzM+Xnpeur4Peh18v+s0v+5zvezti21p5BRVkBDmz8Q+bRo0DreYm5z3q/2azMAn7InvB17+aiaJq33pgAHtI5hi6h/3+Ld7zUHmRqUdc1wJSoElKOWk/509T3+/j+vfWU9JRZVb24kWRgtG+oepNzBAMqWEEMKKBKWEaCjrTCmAEFMWhASlmo85U8oq+CDNzp3Tgk6BMZaGqwBRplKqgvS6+0DZ7E+bea+b7XLt+7ouKovdmGJdo/VzKardU2rRuhSqjQpDO0baZjo5EOLnzf1j1M/++m9HyC0q51Cm+vl728y8pzU5txeU0jJ+as7AFwaAn7eeWy5uX+dYXNGnbRh3XqrOmvf4t3vJL62sc5uqaiPvrlZLC++8tCPehmY6BVAUS/me00wpCUoJK14+kDhYfX5ynVubPjohmfAAbw5mFvKxqX9ao3KnfA8gUMuUcj0AkV9SyeINJ9h68hybUyRwcV7TAk9acNL6uWTPCSGEBKWEaDDrnlIAIZIp1axKz6kNlMHS4BwsASop37PPXukeqAEqV/tAWXNYvufiDHxaOUx9MqVqNDovLKvks83q7Ft3XFp3lpTmxsHt6BQdyNniCuZ8vouKaiPBfl4kRliV/p1xISiVsdum3Oiq3vGM6BzF41f1IDzQx+Xx1GX2mC50jAokq7Cc55bWXca3dE8Gp86WEhnoww01ZiJsUudS1H4qBl/bPlwa7ed27oSl74oQYFvC54bwQB8eHK8GxP/5+xGXgrYuqyq3/M1xN1PKjQDExpRcLtHtYprhZ/bVmBlUnGfMmVJWM51K+Z4QQphJUEqIhqiuslxMm4NSppIY6SnVPLSgU1As+ARalptn4JOglF3mcrsutV9zt4SvLN8yC2XN/WlBiLxUdYY9R7S+UK5mHlivW6N8b8mWUxSWV9EpOpBRXV3fn5dBz9yr1PGuOqyOp1ebUEuT86pyy8/NXvleTHe1AXpJjs1McqEB3nx8+xD+0khZUho/bwMvXd8HnU7tn7P6sP3eWgCKovDOSjVL6rbhSfj7NKyE0C1aP6nYnpYm+NYCIiCkrfpcy0QTAqyana9zua+UZsrARDrHBHGupNL8u98otL/5em/bLFNnAkzB9hLXy/c2HMniDe83edp7MYXHt7g5SNGi1Jx5D6zK987VXl8IIVoZCUoJ0RDFWYACOoPlpDPYFJQqSPfYsFoVbea98BoZMeGSKeWUox5QYAksZbvY7FwLXgXFgV+NUrnASMv/DWf7M2dKuTDFusZOo/OqaiMfrjsBwIwR7s9yN7pbDMM7Wy4ceiWEWF7MPgTGKvUzhthpVu4TYPl51ijhayoDkyK4dWgSAI99vYeicvu9Z1YcyuJgZiGBPgZuuTipWcZm5qzJuUZK+Ny2evVqJk2aRJs2bdDpdHz77bd1blNeXs7cuXNp3749vr6+dOrUiYULFzb9YOsrYQAYfNSgt3asd5GXQc+jV6iZmgvXpZCeV9o4YzKX7kW7PilDPTKlUo7sI0xnCuRrvezE+UnLhgqwzpQyBTSlfE8IISQoJUSDmPtJxYHe9N/JXL4nmVLN4lyNmfc0WqZUWZ7cibTHUfkeQJSWKeVi+Z62XrSdfYFVCZ8LQSm3ekrVbnT+095M0vPUErVrL3J/ljudTsfcK3uYrzVtZ97TSvd6O74YddDsvCk9NL4bbcP9Sc8r5aWf7ZdJapkif7m4PaEB3s02NsB5k3ONNDt3W3FxMX379uXNN990eZsbbriB33//nQ8++IBDhw7x6aefkpyc3ISjbCBvfzUwBW6X8AFc3j2GiztGUFFl5NVf3JxR1JGievS/c7PReVZBGSHnLFmD4SXHKSxrxBJE0bzMmVJWQSntd6KyBCrLmn9MQgjRgkhQSoiG0Ep0tItjsGRQSPle8zh7Qv0aUSNTyjfIUt4lM/DZMlbDWVM5i/XMexotuORqUEoLNtkLcAHEuNBXqiGZUqVnoboSRVF4f42aTfGXi9vXe5a7Hm1CeGh8Ny7uGMHoZKsLT2cz72k8EJQK9PXixevUoM7iDSdrNUXecuIsW06cw8egZ7oLMxE2KqMRMnapzyVTqlFNmDCBZ599lmuvvdal9X/++WdWrVrFsmXLGDNmDElJSQwePJhhw4Y18UgbyNxXyr1m52AJMgN8szPdPKNmg9RnUgY3G52vP5ZLL70ly7eLLp2DmW5MPCFalhI7jc79QtUse5AZ+IQQrZ4EpYRoCHOT83jLMu15YYZ6QSaalpYpVbN8D6TZuSN5J6G6Arz8INROw2stuHT2OFS7cHfenHXVzf7rrjQ7ty6JcZV/hOWkvjibrSfPsSstHx8vPbcMbVj/prtHdeazO4cS4meVVaSV5MX2dLyhOSjVPOV7muGdo/jzIPXf8uEvd1FaUW1+7d+mLKnrBiQQG+LXrOPiXAqUF6i/a9FOMnK0TKnsg2rvLtHovv/+ewYOHMhLL71EQkICXbt25cEHH6S0tJHK2ppKe1PQrB5BKYDebUO5ul8bFAWeX3YAxc3eVLU0JFPKxZ5S647m0FtnG5Ta1xgBNeEZWtDJutG5TmdVwidBKSFE6yZBKSEaoubMe6BmTen0au+ZYseNh0Uj0QJONTOlwBKokmbntrQgUmRn0NvJJgpJAO9A9XfYlYBejilTqs7yPQdBqepKy0m7Oxd6er0liFV0hvdWq1lS1/ZPICrI1/X9uEJRnM+8p9GCK2ePQ3nzZjb8/aruxIX4cSK3hNd/U7PcDmYW8PvBLPQ6uOvSTs06HsCqyXkvMDgpGwxtq16gGasgq+6ZBIX7jh8/ztq1a9m7dy/ffPMNCxYs4Msvv+See+5xuE15eTkFBQU2j2aXOFgNPuelQt6peu3iwXHd8DHoWX8sl5WHGvh32RxAdycopTU6r7t/kKIorD+aQy/9CfOyRH02R05lOt5ItGz2Gp1bfy99pYQQrZwEpYRoCOueUhqDl6WcT5qdN63KUig8rT63lyml9ZWSTClbzmbeA/UOrvZaTh19WKoqLD9fR+V7WlDq3EmoKKn9unZCrtO7PpuVxhTEOnXqJMsPqL2lbr+kCUrUis6o49Tp1Vn2HAmMskx20MzNiUP8vHnuGjVg9v6a4+w8lWfOkprQO56kqEBnmzcNV5qcg/o7J32lmpTRaESn0/HJJ58wePBgrrzySl577TUWLVrkMFtq/vz5hIaGmh+JiXYyK5uab7ClH1k9+koBJEYEMG14EgDzfzpAVXUDspjrU74XYFW+V0cG9cncErwKThCiK0Ex+FDhEwZAUfqBegxWtAj2yvfAagY+yZQSQrRuEpQSoiG0BsvWQSmAENNFqfSValparyjfENsGohote0p6StlyNvOeJtrFZudnj4NSDT7BtmWs1gKjTGULiv39aZkHAVH2M7ecMQWAFy/fjKLAmO4xdI4Jdm8frsg0ZUlFdlabLzujlfB5ILhyefdYJvdrg1GBv322gx92q8egv470QJYUuNbkXCN9pZpUfHw8CQkJhIZamvd3794dRVFIS0uzu81jjz1Gfn6++XHqVP0ylRqsgSV8APeM6kxYgDeHzxTx5Tb7n9clRfWYlEELRijV6uQbTqw/lmsu3dPF9sQYo5YL+5w7TEWVtAQ4L9lrdA6Wcj4p3xNCtHISlBKiIcyZUjUuxrXvC04373haG+vSPXuzoYVLUMouZzPvabRMqew6glJaJlVUF8cz0ul0luwiezPw1afJuUmZr3qx512aTafoQF66vq/b+3DJGa2flJPSPY2HgytPTepJVJAPJ3NLqDYqXNo12nYWwebiapNzTZzp304ypZrE8OHDOX36NEVFReZlhw8fRq/X07ZtW7vb+Pr6EhISYvPwiPYj1K8NCEqFBngz6zL1uPba8sMUl1fVb0fazSh3yve8fNSbJ1BnAGLdsRxLk/P4fvjGq43aO5HGkSxpdn7eqa6yBCJrZUqZMoMlU0oI0cpJUEqIhrA3+x5YZuCToFTTctbkHCyZUvlp0jzZWl3le2BpWl5XppT2erSDJuca7fVsOyUoWlAqyL2gVGFZJT8cUy8sk/yK+WjGECICfdzah8tcmXlP46Fm55rwQB+e+ZNlnB7Lkjp7DCoKwcvfcRN8a1ow78xedYZI4VRRURE7d+5k586dAKSkpLBz505SU1MBNctp6tSp5vVvuukmIiMjue2229i/fz+rV6/moYceYvr06fj715H952nthgA6yD0KhWfqvZtbLm5Pu4gAsgrLec80U6fb6lO+B5YsGSfNzo1GhQ3HcumlNTlv0w+d6djZWZfGvtMe6OklGsY6M84vzPa1APdmZRRCiAuVBKWEqK/qSig2nVzWzJQKkUypZuGsyTmomTfegYCiNskVUJxrKSWI7Ox4PXP53hG1ybcj2S6UAoJVs3NnmVKuX+SVVVZzx+KtHChUL6av7KCnTVgTXlhnutDkXKMFpbL2uzZ7YRO4snccj01I5qHx3bi4o53S1uag9ZOK66322qtLZGfwDoDKEsg91rRjuwBs3bqV/v3707+/moU2Z84c+vfvz5NPPglARkaGOUAFEBQUxPLly8nLy2PgwIHcfPPNTJo0iX/+858eGb9b/MMt//cakC3l46Xn4SvUY9u7q4+TVVjm3g6qyqHMNAue20GpupudH8ws5GxxOb21Jufx/cxZpl106eyXoNT5R/v39gurfRyU8j0hhADAhbNEIYRdRVmAAnqv2inZWqZUoQSlmlRdmVI6nRqwOrNXDWA5ywxqLXJNpXuhieDjpPF1eAd1xquKQrVMVeuTVpMr/anA+Qx85tmsXMuUqqo2cu//drDx+FkSfdWT+qDKcy5tWy9V5ZbP6UpQKixJ7bFVUahuF9uz6cbmgE6n4y5PZUhpzP2kXCjdA7WfWGxPSNuilj46ms1RADBq1CgUJwHjRYsW1VqWnJzM8uXLm3BUTShpuFpGe3I99Lq23ru5qnc87yemsPNUHq8vP8L8a3u7vrF2rDL41M56qYt2nlDsOFNq/bEcEnVZhOqK1feI6QHlaiAqUZfNkfQzQPMfT0QDOJp5D6TRuRBCmEimlBD1ZS7di1OnprcmPaWax1lT+YWjTCmwzMB3TmbgAyzBFWdZUqD2QInoqD63l90Eas8grT9VneV7pqDU2RR11kRrbpTvGY0Kj3y1h98OnMHHS89t44eoLxTVv6SnTtkH1QbFfmGOg3PW9HqPl/C1COaZ9/q5vo15Br5djT4ccZ4zNzuv3wx8Gp1Ox+NXqdlHS7akcuSMG32atNK9wBjHPfQcCaw7U8q6yTkxPdTjcGAUVX4R6HUK5RkHMRqdZK6Klsc8856djFXJlBJCCECCUkLUX5EpKBUcW/s17cK1IMN56ZOov+oqS0meFjyxRwtKnZWgFOBak3ONto6jvlIF6VBZrGYLaj9nR4JiTJkFimUMGhcbnSuKwnPLDvDV9jQMeh1v3XQRPbqYgmtaBkNT0Er34nq7fiHa2oNSxmpLo3dXM6XA403iRQvWfrj6NWtfgy/iByZFML5nLEYFXvjJTvamI9pxxs3+d4BVTyn7QanKaiObjufSW2/pJ6XRx6pBtMSqVFLPlrj/3sJznGZKRdquI4QQrZQEpYSoL0cz71kvqyw2p96LRlaQBsYqMPhCsJPslQiZgc+GOSjlQiljdB1BKW15RCcweDvfl9UMfIf2buWFnw5y1T/XMOLFP0hLOwlAvj7c6S7eWnGUD9aqF2wvXdeHsT1iLX1dyvOh0s3+MK7Smpy7Urqn0YJSrTXjJ/coVBSpPaJcCYBqzJlSuyWgL2wFRlka5qduqHt9Y7U6yYWD48IjVyTjpdfx+8Es1h9zXFJno8gqU8pddfSU2p2WR3FFNf29TqgL4vuZX9ObMk276NOl2fn5RivNk/I9IYRwSHpKCVFfWvlecFzt13wC1KyQsjy1hM/PA9OxX+i0zKfw9rXLJ61p/aakfE/lag8o63Ucle+5MIufoigcOlPImsM5dDobwWXA8lUr+XeVJcPQ4JsLOvjLZ8epWrmGS7pEcUmXKAYlReDnbQDgo40neeVX9f2emNiD6waYprD3C1N7r1RXqKU1Ye3q/lzuOmPKdnKnN5Q542ePGlxxt9TnfGduct5H7RXlqpgeai+z0rNqJl5o26YZnzg/tR8GOYfgxDpIvsr+OsU5sH0xbF0I+afUZYHRaq/H0LZqP73QBDqGtuXhXuW8v7uKZ77bw8MTejC8c5T5mGN/3/WceQ/q7Cm17mguoKhNzo3Ylr1aNTvfcTqfq/rYuRkmWiYtCOlv56aLVr5Xlq9mf7syIYQQQlyA5OgnRH1Z95SyJyTBEpQynVCKRlRXk3ONdaaU0eg8gFVDSUUVx7OL6ZVwgQQVq8otGWMuBaWsZuCzRwtKmfpJVVYbySkqJzO/jOPZxaw7msOaozlkF5YDMM0QyWXe0Ms7g+v6tOWSLlFEBnoT82mBWtWnhJKRUcCBjALeXX0cXy89gztE0DkmiEXr1XHPuqwzM0ZY/ZvrdGrWQkEaFGU3flBKUazK99zIlIpOVssay/LUbI2wxMYdV0tnbnLez73tvP3Un13WPjWgJ0EpYS1pBGz70P4MfOnbYPN7sPcrNUgNoNODYlRLhIuzIWOnzSZ3Anf6wcm8GK7+7z8o8w5leKcoLusew2XJMcSH1pjRs6gBQak6ekqtP5ZDW102gcZC0HurAVqN6RjbRZfGx5IpdX4pcZIpZR2oKsuz/I4IIUQrI0EpIerLWaYUQEi8emElzc6bhpYp5azJOah3xXUGqCpT+4C50qja5P4lO/ll3xmeu6YXNw9p34DBthBnU9SG3T7Bjn9vrWkZUEWZUJaP4hvCxuNnOZFbTGZ+GVcf2E5H4JXt8NmG38gtLrdbceXnrWdIh0gGRw6FHYsZGZ7LqBv6qi+WngOlCoAfH53M2hOFrD2Sw5ojOWQWlLHG9BzglovbM2esnWBakBaUaoJm54WZataOTg/RbgSXvXzV4MqZvWp/pFYXlNKanLvRT0oT30c9dmbshm4TGndc4vymNTvP3A1lBWqW5L5vYMt7alBK0+YiGHwn9LwGKkvUrLv8tNqPgnSUgtO012cxN/B7Hiq+md8PZvH7QTX41CM+hMu7x3B591j6JISib1D5nuP+QaUV1Ww/mcflWpPz2B7qMURjKt9rp8viaHq2++8tPMdZo3ODl5pJX5av/l5IUEoI0UpJUEqI+jIHpRyk0WvBD633lGhc2sx7dWVKGbzVgMC5E2pQxsWg1MHMAn7ZpwY5nl96gFHdYkgI869jqxbOutzOlXIyvxD197swA7IP811OG2Yv2Wl++S++x0EHq86Gk6Oo2VBeeh0xwb7Eh/kzuEMEl3SOYkBSOL5eBihIhB2gO5ei9nnx9lOzmwB8Q4kMC+HqfiFc3S8BRVE4ll3E6sM5rD+WS8foQB69IhmdvXFrWQtNEZQ6Y8qSiuyijtcdcX1MQak9jkuNLkT1bXKuiesNuz6VZueitpA26jH/XAp8d4+aMaUFeQw+0Os6GHQHtB1g2cbbTw0IaH3eatAdXwmLr+Z6488MuPVBfsoM4Y+DWWxPPcf+jAL2ZxTwrz+OEhXkw/eBqbSBhpXv2QlKbT15lopqI0MDT0E1Nv2kAAiMRvGPQF96ltCSE2QVlhET7ObxSHiGs0bnoJbwleXLDHxCiFZNglJC1Je50bmd2ffA0ny7IL15xtPaaGVozmbe04QnqeufS4Gk4S7t/t1Vx83PiyuqmfvNHj6cNsh+UOR84U4/KU1UVyjMQMk5xAdr1cBT38QwBkRD9H61jOTBm64iMiKS2BA/IgN90Osd/IyC4yx3hXOPquVwxfZns9LpdHSOCaZzTDDTR9QReNQuEIubIINAmz3PndI9TVxv2EXrm4Ev57CaneIdCJGd3d/eutm5EDW1H64eyw98r34f0hYGTYeLbq1fpknHUdB1ArrDP9Fx+wvcc/Pn3DO6M7lF5aw8lM0fB7NYfTibnKIKSisy1CmCGhKUqiiyBOVN1H5SMMQvFYqB+L622+p06KKTIXU9XXRp7DtdQEw3CUqdF+oKSgVEqL/P0uxcCNGKyex7QtRHdSWUmJqV1pUpVSCZUo1OUVwv3wOrZucnXNp9el4p3+9Syy5fn9IXH4OelYey+W7neV6K6c7MexpTACvr+G72pOfj46Xnw2mDePJi0z2NkARG9u5Ir4RQooN9HQekQM3OMpWhkG2ahl0LJAXWY4p1TWBTZkppM++50eRco2VmtLaMH62fVHxf95qca7SfW36qZA+I2i66RQ1udxgJUz6Bv+2CSx5oWOnTuGfVHnBHfoGjvwMQGeTLdQPa8tbNF7HtibHcPaoT0bp8AA4U1iMg5BeqvgfUypbacCwHUEiqMB2j7fVi0/pK6dPZL32lzh9asMnfTvme9XI51gkhWjEJSglRH9rFr97b8YmGOSh1ngcyWqLibKgsBnSuNbbWAldaIKsOH6xJocqoMKxTJNf0b8t9l6vZHs/8sI/covJ6DroFyNWCUm5kSpkuhLJS1DK2SX3aEBHoU7+sK6v9mYNSRY0QlAoyZStq/V4ak1a+F2u/9McpLbsqL1XtndVamPtJ9avf9v5hEGbq4dbassxE3dpdDI+mwq3fQ/eJjTNjWVRntQcVwC9z1ZnQrPh46XnwsvaE6EoAmPX9abIKy9x7D53Obglffmkle9LzSSAH38p8NXAVYycIbjUD377T+e69t/AMYzWU5qnPHWZKOS7rFEKI1kKCUkLUh3nmvVjHs7mZe0pJUKrRacGl0La2zWAdMWdK1R2Uyiup4LMtqQDcNbKT+WtyXDDnSip55of99RqyxylKgzKlggqPAXDrMFOwIOeQ+lULMrlKaxZeM1OqPuUwGnNPqUYOSlWWWX5m9Snf8w+3BE21Gfxag4Y0OdfEm0r4WluWmfCckQ+r/2ezD8D2/9Z6WV+iHqsq8OJooZ57PtlORZXRvfcwByByzIs2Hs/FqMCYMFNWdUx3+/3rrGbg2yeZUueH0jzANPuH9Ux71rQG6FK+J4RoxSQoJUR91DXzHljK+kpy1Ytb0XjMTc6TXFvfjUypxRtOUlJRTY/4EC7topaDeBv0vHx9X/Q6+H7XaX7b3wRlYk1EURSqqo1qdl95gTqLnCt9uDSmoFQiWQxoG0iftmHq8voEuMASxMrSglLabFYNyZRqovK97IPqbIX+4Y7LdOui9UdqLRk/1VWWz9qQoFScqaeO9JUSzcU/HEY9pj5f8Zwlw0VjyurUBcUS7OvNlhPnmPfjPvfewxyUsgQg1h9VA1SXhZpuYNVscq4xlT63150hMzePwrJK995bND8t+8kv1HFG34VcvnfgB8g64OlRCCHOAxKUEqI+zE3OnQSl/MPBy992fdE4tIwnV4MrWvCq9KzaZNuBsspqFq0/AcBdIzvaNDXv3TaUOy5V3+/xb/dScJ5cEExftIVhL/xB9glToCA8ybXsMpOqgBiKCMBLZ2SmdbJQtilTKsrNTClTCQpnj0NVORSbMgYao3yvsRudm0v3erk2W6E9ra2vVM4hqCoFn2CI6FT//UimlPCEgdPVQHxJLqx5xfY1UwDdOySWBX/uh04HH29M5bPNqa7vXwtKFVsypdYdUwMXPTDdbHFU9hoUC35hGHQKHXSZHMgodP19hWfU1eQcLJlSF1pQ6sx+WPIX+GKap0cihOcpilrOKxySoJQQ9eFKppROByGm7ArpK9W43GlyDuAbDAFRttva8cXWU5wtrqBtuD9X9a6dGXP/mK4kRQaQWVDGiz8ddHfUzS49r5QVh7LJKiznjzXr1IVu9oD67WA2R43qz2JkhOmkubIM8k7Wa38Ex4NviJqBlHvUUnLXoEbnpm0riqCiuP77qUkruXMwlbxLWlumlE2T8wacYmg/t5zDUFHS4GEJ4RKDN4x7Tn2+8d+Qe8zympaJGRTD5d1jmTNGPfY9+d0+tqe62DNOa8ZuClZkFZRxNKsInU4hssCUURLvIMPQaqKIrro06St1PqiryTlcuOV7Wol+7lG5GBetm6LAhxPg7aHqRFnCLo8Hpd5++206dOiAn58fAwYMYM2aNU7XLy8vZ+7cubRv3x5fX186derEwoULm2m0QpgUuRCUAgjW+kpJplSj0jKlwl0MSoElgOVgBr6qaiPvrVH3e8clHfEy1D48+nkbmH+terH8yaZUNh5v2Y1J/zho6bFUctrUC8vNcrvFG05wVEkAwOec6QLt7DFQjGpJgru9oHQ622bnWvleQ3pK+QZbshIbs6+UOVOqHjPvabSAVvZBNTPsQtfQJuea4Dg12KgYIes87eMmzk9dxkKny8BYCcuftCwvsu1/d8/ozozvGUtFtZGZH20jq8CFMv0aTa3Xm7KkRsdWoC/NVZucOzvemI6dnfXSV+q84Eqm1IVavpd/Sv1qrGqamXGFOF+U5UPqBjWT/NxJT4+mxfJoUGrJkiXMnj2buXPnsmPHDi655BImTJhAaqrjVOgbbriB33//nQ8++IBDhw7x6aefkpyc3IyjFgKrTKk6+szIDHxNw91MKaiz2flPezNJPVtCeIA3/zewrcPdDO0UyU1D1ObVj361m7LKlnsHcIUpKBUW4E0nnfo7aIx0PSh1NKuQ9cdyOa6Yfo+15ubm0r2u9StrM93tJ/tQ45Tv6XSN3+xcUWzL9+ortC34hakn5tkNzK7LS4Wdn8J398DXdzZOBlHJWfj3JbBifsP3BZZyO0d9cVyl01mypTJ2NWxfQrhDp4Pxz6v99w7+CCmmm6XahXWgeqzR63W8ekM/usQEkVVYzsyPt1FeVcffgxqNzteZ+kldFW3ad7SDJucamxn4JCjV4rlTvnehZUrlWV3L5ad5bhxCeJp2zQiSpOCER4NSr732GjNmzOD222+ne/fuLFiwgMTERN555x276//888+sWrWKZcuWMWbMGJKSkhg8eDDDhg1r5pGLVs88+14dmVJSvtf4ygosMxfVJ1PKTvmeoij8Z7U2u1wSAT7Opxh/dEIycSF+nMgt4fXfDrs+BhcczSpk7Gur+GFXw35nSiuqzRc8b954EZ316h/CVWcdzABkx+IN6h2dwIQe6oIc02c1Nzl3s5+URgtKpW9XS+6gYUEpaPxm5wWnofQc6AyW8daHTmfJlnKnabeiqKVD2xfD13fB671gQW/4dibs+Bh2L4ED39d/XJqjv6mBpM3vqu/ZEEaj2kcE6jdbYU3SV0p4Skx3GHCb+vyXv6vlR3ayOoN8vXh36kCC/bzYnprH09/XkdVn1ehcURRzptRAH9MFfJu+zrc3ZUp11aVxNKvQ/dn/RPPSsp8CnJXvWTW/b+gxuCXJO2X13I2+a0JcaKwDURKUcshjQamKigq2bdvGuHHjbJaPGzeO9evX293m+++/Z+DAgbz00kskJCTQtWtXHnzwQUpLS5tjyC1XdRUc/R3Kpeml206uVy883eVKTymAkATT+vUIMKRuhOJGKg8ryoZTmxtnX6Duq7EyUopz4cQ619fXMp0CIsEvxPXtnGRKrTuay970Avy9Ddw6NKnOXYX4efPsZPWi+/01KexJc7G3R9rWOk/OPt+axpGsIl7/7TBKA05QNxzPobzKSJtQP4a386ONTg1QPb+pmuLyqjq3Lyyr5Ktt6t3NIYNNgf+cI2rgQcuYinazn5RGC/KkblC/evmpJXgNYW527uT3UlHUWf/2faP2PnJWTnfGNKtWVBfnmQuuiDddaB5aBvu+hb1fw96vYM+XsPsL2LUEdn2mZkFtehe+nA6vdYd/XQTfz4Ldn6mlEHovaDsIYk1BLi2TqyG0fZSebXhALz8VKgpB7w2RnRs+NnOmlASlhAeM/jv4hqpB0Z3/q1W+p+kQFcg/b+yPTgefbk7lk01OyjOsGp2nni0hPa8Ub4OOtqWmY2pdGYbaDHz6M+iqKzh8Rs77WjRXglJa+Z5S7XQilvOOZEoJobIOREmSgkPO0wGaUE5ODtXV1cTGxtosj42NJTMz0+42x48fZ+3atfj5+fHNN9+Qk5PD3XffzdmzZx32lSovL6e83HLhUVBwAaY77/lCvXs+ZCZMeNHTozl/HPkNPrkOel0H17vRl6yqwpKpU1f5XnA9M6WOr4TFV0Ony+GWr93b1p4vpsHJtTD1e+g4smH7OrEWFl2lXhzf/lvDx/bVDDi+Aq55F/pOqXv9s27OvKcxZ0qdqPXSv1epWVJTBiUSHujj0u7G9IhlUt82/LDrNA9/tZvv7x2Ot50+VGbazy0oFu7dovZjsmNnah4Ax7OLOZhZSPd4NwJvVrR+UqOTY9CdVWd1yiOEI0U+vLPyGA+Od57l9M2OdIorqukYHciAfv1gqTdUlkBBuiVjyt0m55oYU1DKOkuqvrPbabRMK+tgqaKoJYIn1qg//xNrLf93QQ2eRCerGTlxfdSMprjearDzjKkxeUNK9zRaptShZerDVQYfSBgA7YdD0nBoOxh8g2Drh/DjbEvgrCGs93Fmb92Bdqf7MmWJRCerDaMbKtI0e5+cxAlPCIyCSx+E5U/AH/9Qg8JgLt+zNrpbDA+N78ZLPx/i6e/3EeBjYGKfNrX/Jlg1Ol93VL3p1D8xDEOmqUS1jYMm55rgePANxas8nyRdJvtPF9Arwf7fEtECuFK+5+0H3gHq39fSs+Af1ixDa1KKYukpBRKUEq2bTaaU/RiH8GBQSqOrcSGiKEqtZRqj0YhOp+OTTz4hNFT9I/zaa69x/fXX89Zbb+Hv719rm/nz5/PMM880/sBbEi1roTEuUFoTLUvj2Ar1D6irF8VaNoHe2/ndL7BkShW4ma55+Bf16/GVaiaXv+slV7UUZakBKYA9nzc8KLX7c/Vr2hbIOQpRDciIKMhQPyPAyvlqgNBQx2GpPk3OAcKTTO+ZpgYWvdTg0970fNYezcGg1zFjhHv7fGpSD9YeyeZARgHvrj7OPaMd/CyMRvhlrvq86AysfBGueL7WapXVRnan55m/X7o7o15BKUVRWHFQvat/efcYyFF7oiiRnSEd3l1znCmDEkmMCHC4vVa6N/Xi9ugM3mqAIPug+sg5qq5Y36BUSAL4BDVe6R5YMqUy98Lm9+wHoUBtiB6TrAY3y/LU4NOZPcAnlnXCO0B1hfq8IU3ONclXQY+rofCM2qdGp7N8RWe7TO+tZlYlDVcDv961/66ZA2WNHpTaB53HNHxfsT0aNiaNFrgty2uc/QnhriF3wdaFthm2QbF2V/3ryE7sSy9g6Z4M7l+yi/nLDnLj4HbcOLgdcaGmbEurRufrj6oB9PGJ1bAlRy0Vrut4o00UkbbZaga+xAZ+SNFkXJl9T3u9sgRKzkEdq54XSs9Z/r6DBKVE62Z9DVifyplWwmNBqaioKAwGQ62sqKysrFrZU5r4+HgSEhLMASmA7t27oygKaWlpdOlSu4HvY489xpw5c8zfFxQUkJh4gf0B11LKC9I9O47zTZZp+uXSs3D2uOWufF20oFRwXN2BLK2nVGGG2pNCb3DtPU6YgkhKtRo063Wta9vZc/R3y/ODS2HigvpnMVRXqY1fNfu/gUsfqv/YDnwPmErUzqWoZUr9/+J8mzqanBeVV+Hnpa89e15QrOVuZP4p87+3liU1sU+8wyCNI1FBvjw1qSezl+zkjd+PcOPgdkTYy7Ta+yVk7ASDL1SXw6Z/w0W3mJvWag5lFlJWaekR8uPu0zwwrqvDQL0jh88UkZ5Xiq+XnqEdo2Cd2gMqrF1PhvpGsuF4Li/8dJC3br7I7vYbjuVyNKuIQB8D1w0wNX2P6qIGpI7+DlWlahZPWHu3xmWmXVilb1O/b5SglCl74dBS9aHx8od2QyBpBCRdAm0uUgOS2p3cjN1qeU7mHvV5QZrtBWhDZ5EDtTTxhsUN348mpjugU49FRdkQVM+fX3Gu7R28hga5srSgVCME8sASlKoqg8qyhpdRCuEuL18Y9w9YYvV3ycH/N51Ox6s39KVTdCD/25xKVmE5b/x+hDdXHGVcj1huubg9Q9tHoANQqtlz7BTgw8hg07lbdLL9IHRNpqBUF306a6TZecvmSqYUqDc4C9Is65/varYpkKCUaM0kU8olHusp5ePjw4ABA1i+fLnN8uXLlztsXD58+HBOnz5NUZEl+n748GH0ej1t29qfLcvX15eQkBCbxwVHC5IUnL6wmiQ2NetpxrWLY1doBxdXylyCYtW7n0o1FGe7tv/Sc+oFsubIcsfruuLIr7b7PrGm/vs6uc72pGnfd/XfF6j9dcDSY2jVS1Bd6XwbJ5lSG47lMujZ3xjx4go+2nDCdiYknc6SLWUKbKXmlrBsj/rvedelLgYla7i6Xxu6xgZRUWVkc4qdE8rKUvjNlK056hFInqj+Pix7qNb/152n8gC4qF0Yvl56TuSW1GuGpd8PqseEYZ0i8fcxmMvtdFFdeXJSD/Q6WLong80p9mf70bKkrrkogWA/UwBTa2quBSUjO9ed1eZMtFVArr5BFWvthqr/17z8oeMouOxxmP4LPJoKU79Tg6ftLjZnyKHTQVg76D5R7R1z46cwZx88dBxu+RbG/gOueAE6jm742Bqbb5AlKJvVgEBSzW0bGpTSyvdiGiko5Wv197pcLr6FhyRPhPYj1OcGX9vfyxr8vA3MGdeN9Y9ezj9v7M/gpAiqjQo/7c3kpvc3MeafG6kwBAKgL80lwMdAhwpT5qmrAXDTzYzOujQOZBRgNMp5X4vlSk8p69cvlBn4tNI97f+KdSmfEK2NTU8paXTuiEdn35szZw7vv/8+Cxcu5MCBA9x///2kpqYyc+ZMQM1ymjp1qnn9m266icjISG677Tb279/P6tWreeihh5g+fbrd0r1WQwtKVZVdOHdZmlpFCZw7Yfk+bavr27ra5BzUzCgt1d/VTLbUjYCilvEAHF2uln/VR3UVHDNlSml9bfY3YMYubbav5IlqAODMHkspl7sKMiwllFM+Uft05J1UG8o64yBT6mhWIXd9tJXSymoyC8p44rt9jHp5JR9tPGkJTtVodv7emuMYFRjZNZoebeoXsNbpdAzpoN4F3XLCTtP8je+od0BDEuDiu9Wpxr381ODgvm9sVt1h6ic1vHMUlyWrmT9L97j/B2yFqZ/UZd1Nv3vm2fK60j0+hCmD2gEw78d9tS5oTueV8ut+9Xd8qnXTd61UTzu5jKqdmeqWaKueVo2RKRXbQw1AOQpCuSowEjqNhuH3wcV/bXivq6aiZSNlNqDZuRaE0hqxZx9SS1vro7IMco/ajq2h9AbLRc2F1ABYnF90OrXc2stP7fnkwjHBx0vPn/q24fOZQ/l59iX85eJ2BPoYOJZdTGalmpEbTiGDO0RgOGPqJ1VXk3ON6djZTa/2/Tt5tqQ+n0o0NWO1ZSKdujKltPK+kgskKKVlSiUOUb+W5clkTKL1ss6OKsyQBBIHPBqUmjJlCgsWLGDevHn069eP1atXs2zZMtq3V0tCMjIySE21pIAGBQWxfPly8vLyGDhwIDfffDOTJk3in//8p6c+Qstg3dhX7ka4JucQ5rIxgHR3glKmIEGQiw2BQ9qoX12Njmule32mgE+wmmGVsdP18VlL26xezPmHw+VPq8sO/KCeLLnLWK1uC+pU2R1Hqc/3f+NwE6e00r3EIWpfqhGz1eWrX3F8YVxVbkkDt8qUyi4sZ9qHWygoq2JA+3DmXd2TuBA/MvLLeOLbvYx+eSWfbDpJdViSusHZFHKKyvl8q/r/5a6RbjZNr2Fgktrza+uJGieUxTmw5jX1+eVPqqUZ4e1hhKmk+NfHodyS+bnzlHoC2y8xjKv6qKWfS3dnuDULX15JBdtOqvu5LDlGDWjmakEpNZD0wLiuBPt6sTe9gC+32abV/29TKkYFLu4YQddYqxnxas60F9WNBrEuXbTTOLhefIPcD0Kdrxqjr5Q2816X8eosY8ZKy++Ku3IOqRmA/uENa5Zek1+Y+lWCUsKT4vvCrO1w8xdub5ocF8Kzk3uz8e+X84+re1Lirf69iNQVcGnnKHUmUHA9U8qUWZyky8SbKlNfKdHilOZhPs+sqy/ohZYplWe6FonpbinDlhI+0RoZq22DUsZKSSBxwKNBKYC7776bEydOUF5ezrZt27j00kvNry1atIiVK1farJ+cnMzy5cspKSnh1KlTvPrqq607S8potC0Ly5e+Ui7JOqh+DVUzRsjc43x6eGuFVj2lXKH1lXJ1BimtvK7TaOg0Sn1e3xI+rXSv8xi1wblfmNr4+eR69/d1apOalecXCh0uhZ6T1eVaCZ67tCyhHqb9DJyuZpXlp8LOj+1vk5cKKOAdaO4hVFJRxYz/biHtXClJkQG8N3UgU4cmsfKhUTzzp57EhvhyOr+Mud/s5fVtammg8exxFq8/QXmVkb5tQxnasY67mHUYlKSeUO49XUBxeZXlhZUvQEWhOrNb7xssy4ffp/ZjKkiHNa8CkF9aybHsYkANSl2WHIOft57UsyXsTXe9dGnV4WyMCnSLDSYhzF8NVFeV2fSAigry5b7L1QDVS78cosg05vKqaj7drN4IuNU6SwogskZmVHQDg1LW2wc1UlCqNdGykc40QqZUXC+r/dUzyGVuct6rcbPLpNm5aClCE9SZOesp2M+bW4Ym0a1jEgDTLwrhll4+UJylTnLg6kyfIQngE4wX1bTXZdarxFs0Ay3A5Btadx9Pqwb4FwQtUyqsHYSa+vhKUEq0RsU56g07nd4SnJYZhe1yOyiVlJTEvHnzbDKYhAeVnlV/2TXS7Nw1Wj+pruPVtOnqCtfLYMw9peJdW1+bgc+VGRdK8yz9pNoPhy7j1OfWfaHcoQWzuoxTT4qSJ6rfH6hHCZ9W9tftSjUbJXmiOkX2mb2W8jBXFZw2lSmizkoGahaRlkG0+lX7QULr0j2djmqjwt8+28nutHzCA7z58LbB5kbjft4Gbh2WxKqHRvP0pB7EBPuyp0T9g5ByZB8frj8BwMyRndxuJF5TmzB/EsL8qTYq5r5Q5BxRZ20CGP8c6K0Ot97+ar8igPX/gpyj7E5Tt2sXEUBkkC8BPl5cnqyW3/24x/U/YH+YSvdGm8r/zP82EZ1sekDdOiyJpMgAcorKeWuFWnb1055McosriAvxY2yPGhNO+AZBiFXvvoaW74W0VYOLYJkmXbhOCyJlH1TLdN1lrLZM9hDbq+FBLi0oFdNIM+9pzEEpyQYRFwZdgHq8GxoH3md2qwujk8HHxYk2tIkiwDQDnwSlWiRzk3MXptO70Mr38q2DUqbzBqnkEK2Rdu0XGGP5vyDNzu1yOyj1wAMP8N1339GxY0fGjh3LZ599Rnm5ixkmovFp/aQ0cifCNdmmTKmY7pAwQH3uagmfuaeU/Vkia9GCV66U76VuBMWoBhBC4qHzWNPYtqnRdnfkp5suMHXQ6XJ1mRYA2v+9e32qjEZLIEvbR0AEdBipPnc3W2q/VeleaIJl+YBp6s+rIA2225mtzNzkPAmAZ5fuZ/n+M/h46Xlv6kA6RAXW2sTP28C04R1Y/fBoJo1SJ1GIN56hsKySDlGBjOvZOKVGg0wlfFu0Er7lT6kB464T1MyymrpNUP99jZXw8yPsOGkp3dO4W8JXbVRYdVjNnLy8uxaUUpuc1wwi+XjpmXuVGkD4YE0Kqbkl/HfDCQBuHtKu9uyFYFvCVzNzyl16PVw0Ve1V1aZ/w/bVGoUlgU+QGlDPrUdft7PH1Qw67wD1/1OjZUpJUEoIp7QgRXGOpTTf1X5Smhi1hK+LLp39p/PdKvEWzcTVmffgwi3fswlKyfWJaIWs+xAHm9q5uJKk0Aq5HZSaNWsW27ZtY9u2bfTo0YP77ruP+Ph47r33XrZv394UYxTOSFCqfrQMgZju0Hag+tzVZudF2gHG1UwpraeUC1lsJ039pJKGm7aNNzUoV+Dob669n+aoKUuq7UC1eTOoJXy+IepnSNvs+r7St6nj9wm2nY2s5zXq1/3fujc2rXRP217j7QeXPKA+X/Oa2jzZmlWm1MK1KXy47gQAr93Ql4FJzu9G+nkbuP6yYSg6PQG6csa0hX9c3QuDvnFKjbT333rinNoX7NBStRn82Gfsb6DTwYQX1bK6o7/B4WWAbVBqdLcY/L0NpJ0rZXda3RflO1LPkVdSSai/N/21/Zj7SXWttf6Y7jGM6BxFRbWRv36yjR2peXgbdPx5cDv7b6DtI7Sd63f1nZnwAty7xRJ4EK7T6y1ZSfXJbtK2iemuNhRvaI8qLfvU1RIkV0lQSlxotMzQkrPu95PSmPpKddGnk1NUQXah3BxucVydeQ+sMqXsTJZyvikrsJRbhyZK+Z5o3bTqmpA2lrYvkillV717SvXt25c33niD9PR0nnrqKd5//30GDRpE3759Wbhwody1aS5F2bbfS/le3coKLGnE0cmQYApKuZIpVVVuufvlblCq0IVMKa3JedIllmX1LeGzLt0D9f+kl6+aoQPuzcJ34Dv1a9fxauBIk3yV+yV8BafhVI3SPWsXTVVLHgtP186WOnscgH1lkfxjqXoR/OiEZCb2aePae3v5oDPdtXt/UiQjujRe2ZjWV2pHai7KL4+rCwdMc957KbITDL0XgOuy3sKXCvq3CzO/7O9jMGc8uTIL3++m0r2RXaMtmU7mmfdqZzbpdDqemNgDvQ5zCciVveOJDva1/wZaNk1jZ8OI+mlIyZ05s8m0D63xfGEGFLvZ16Q4x3KDxHSx3GgkKCUuNOb+QQ3IlDL9P+vprd5xlxK+Fqg+mVIXQk8p7fzaP0It+9cypfKkfE+0QlqVTHCcVZKCZErZU++gVGVlJZ9//jl/+tOfeOCBBxg4cCDvv/8+N9xwA3PnzuXmm29uzHEKR7QLgYhO6ldpdF637EPq16A49UQg4SL1+7PH667n137eBp+6Z1PRBFs1OncWrC0rgAzT1NDth1uWa0Gpo7+73jumqhyOrzRtP5a53+yh/z+Ws/90gVUJ33euTUuqKOq6UDuIFBBhmYXP1RI+bV+JF1sO0Na8fOESU2+pNa9CZanlNVP53itbKlEUuGlIO+661M2Z87RZ+7RSwEbSJSaIUH9vLq9aiy5jh5pVNuqxuje89EGqguJJIIt7vH+kRxvbRroT3SjhW2EKSl2m9ZMCS1DKQbldt7hgbhpiyYyaOrS94zfodT1c9jiMcZD9JZpXQ0rurBuTg3rxoP3fyHJzf9q+wjuo+2lMEpQSFxpTTynO7FPPKXR6dbIBd5iCUonG03jJDHwtk1aK5+9CptSFVL5nbnJuypCSTCnRmpn7EFtnSrk4G3sr43ZQavv27cyaNYv4+HhmzZpFz5492bt3L2vXruW2225j7ty5fP/993zzTT2niRfu0YIkWk+WwtP1a3rbmmRble6BejKgBfXS6yhB1WbeC4pzfYYpLfBSWeL8wkrrJxXewbbPUsJAdda8sjzX+16lboCKIgiK5ZRvFz7dnEpeSSWPfLWbqqRRaoPpgrS6Py+od3LzUtXeM53H1H5dK8Hb5+L/eS14VbN0z1r/W9QTmaJM2LZIXWY0opw7CcCRqmhGdo1m3p96ut+kPMJ04X22cYNSer2OoYkBPOy9RF0wYjYERde9oU8gO7o/CMBdhu/xLbS9mziqWwwBPgbS80otTdTtSM8r5WBmIXqdmikFQHWl5RihnSDaMWdsN7rEBDGuRywXtXMSbPUJgEsfMvczER7WkJI7LbtKC2xZP3d3fzWzrhqTBKXEhUbLnNEy26O6gk/tfohOhbYFnyC8qKK97oxkSrVE9Wl0XlUGFSVNN6bmoGVEacEoLVOqIF2dYEOI1qTQKlMq2I3KmVbI7aDUoEGDOHLkCO+88w5paWm88sorJCfbXqD06NGDP//5z402SOFEkZoZQVxv0HurQY0iqVV1KqtGUAosfaXqCvpYH1xc5e3v2jSgJ9aoX5NG2C43eEFnU6NyV0v4tNK9zmP5eNMpjKYEmz3p+SzackYtwwPXekFpmU1dxtnvI9TtSrWEL2sfZB92vq/8dKvSvT85Xs/L19Jbau3rUFFCQVYquupyKhUDobEdeOvmi+w3466LqUk65064v20dbvX6hba6HM56RcPFd7u83dKqi1lf3QNfKuCXuTav+XkbGNNdbaq/dLfjP2TarHsXtQsn3DQDoRqQUtRjQ4DjUsWIQB+WzxnJu1MHNngmQtGMtDLKgnT3Zm0qy7fczbaeLc8c5HKzHDBLglJCuKxmOZe7pXug3hQz9fjrokuXoFRLZO4p5UL5nm+w+ncazv9sqTz15iFhpqzr4Di1v6ZS3XJ66VSUwDsj4Lt7PT0ScaHTfudD4tUHuDbxVSvk9hXd8ePH+fnnn/m///s/vL297a4TGBjIhx9+2ODBCRcUm4JSwVa/7FLC55y9oJQ2A19dzc7dnXlPE2LKfHI248LJderXmkEpcL+vlGm98g6X89kW9a7VhF5qIO3VXw+T087UV+rA985L+GxK9xwEkQIiLM3P6wpyaTP4tRtqv3TPWr+b1YbaRWdg60L+9ZX6mTJ1MXxw28UE+Xo5396RJirfoziHQafU494bxiko3v4ub7ozLZ+nqqZh1Bng4I9wxLapvTYL37I9GRiN9v+9tNK90dale9a17Pp6V2uLlsovVJ3dCCyNxl2hHQNDEmzv4tc7U8r03jFN0GtMglLiQhNYI0jhbpNzjekcposujdSzJRSUVTZsXKJxudPoXKe7cPpKaT2ltOxsvcFyDtxSSvjSt8GZPbDrU6kuEU1LS0YIjre0cynJgaoKz42phXL7KiUrK4tNmzbVWr5p0ya2bnWxtEg0Hi1TKigaQrQU2RZy0G+ptAuyaOuglJYptc15kMbdmfc01n2l7CkvtMzCY91PStN5DKCDzD11N8g7mwI5h0Fn4MeibuSXVpIY4c+/buzP4A4RlFZW8/e9sShe/mq2UOZux/s6s0/tteXlZwmM2dNzsvq1rr5SWolfj8nO1wPw8oGRDwFQteZ1Kk/vASC8bVfiQv2cbelcE5XvsepFvCoL2a8ksbjkYk7kupaCX15Vzf7TBRxR2lLUd4a68KeH1b5gJiO7RhPoY+B0fhk77JTwlVZUs+5oDoC5MTpgCYK6+/sqzh/1KeGzV7pn/X3WAdfLLIzVlmNqY8+8BxKUcmL16tVMmjSJNm3aoNPp+Pbbb13edt26dXh5edGvX78mG59wwDdUzRzR1CdTCsyTaPTxVc9LDriQLeXopoZoAu40OgerGfjO90wpLShlNYuvFqDKbyHNzrVZiY1VkJ/q2bGIC1dVuSXzMThePRZoGZFS1VSL20Gpe+65h1Onah9U0tPTueeeexplUMINWr+YoFhLH6KWcieiJSo5azkQWM+KFtdLbV5eetY8w5td5kwpN8r3wGrGBQcpm6mb1NTmsPb2e/8ERlmyuY7+Vvt1a6bXlXYX894W9aRo6sVJeBn0PH9Nb3wMen49UkRGjCkjS8uEskd7rfMYNb3ckeSr1ANt1j5LI/ma8tPg1CZA57x0z1rfGyE8Ca/SHO7x+haAoHj7DbtdpmVKleSowcDGkHMEti4E4IuImSjo2XLCtRPL/acLqKg2EhHoQ/D4uRAYA2ePweb3zOv4eRsY28NxCd+G4zmUVxlpE+pHt1irfyctgBkiQakLlhZIytzj+jaOekCFd1B7x1WVOT8OWjt3AqpKwcvfEvBtTBKUcqi4uJi+ffvy5ptvurVdfn4+U6dO5fLLL2+ikQmn9HqrQIVObb9QH6Zm590Nana8vRK+zPwyfth1mqe+28uVb6yh89xlzPvBjaxKUX/uBqUulGbnWml4qNW5rNZXqqUEpXKOWp7nuvi3Tgh3aS1fDL5qGxedzipJoQWV8H0+Fb6cAaa+vZ7idlBq//79XHTRRbWW9+/fn/375Q9ds6qutPzRC4q1OuhL+Z5D2QfVr6GJ4Gc1y5mXL8T1UZ87a/6tHWCC6huUcvBvY+4ndYnjfbhawmd6PTVyBAczC/H3NnDDQPXkoHNMEPeM7gzAW5mmC1Jns/Bp5Xbd6wgi+YdDJ1MJn6Nsqf1ulO5pDN4ol6rZUtE60wl3hJuz7dXkF2I5SWysbKnfn1HvuHUZj3839eew1cWglNa8vF9iGDr/MLVBOkDKKpv1ruqj/szslfD9YVW6Z9MTypw27OLPW5x/6lNyV3PmPY1ebynBc7WvlLZeTLJaptHYJCjl0IQJE3j22We59tpr3drurrvu4qabbmLo0KFNNDJRJ+1vUFTX+s9YaQpKxVWlYaCavafzOXymkE82neT+JTsZ8eIfXDz/d2Z9uoP/bjjJ/owCjAosXJfCV9vk5mWTMlark9OAa7PvgVX53nkclKooUW/4gW2mlPn6pIX83mmZUqDeBBSiKVgnMmjn5tpN4pbS7NxYDQeXwd4v1ZlgPcjtd/f19eXMmTO1lmdkZODlVc8eL6J+ik0Hfp1B/aOn1Ww7CnwI+/2kNK40O29oppSjg9CJtepXe/2kNF3Gql+PrXRci1xRAimrAVicrTZBveaiBEIDLP3fZo7qSOeYIL4r6UWVzhtyj1p+LtayDqpBPL03dLvC8bg0Wkmeo75SWumeVurnosOxV5FitOrhFd4I2RiN2VfKWA2HflafXzaXQUnqieWWE+dc2nxHah6gBqUAiDJl8NUo07ykSxTBvl5kFpSxPdWyb0VRWHEwW317635SYPl9k0ypC5cWWHK15M5otPSAsteY3N0gl7mfVBM0OQdLUKqqDCrLmuY9WpEPP/yQY8eO8dRTT3l6KK1boGniifr2kwL15pp3AF5KJe10WXy9PZ1xr69m7jd7+WZHOmnnStHroFdCCNOGJfHWTRcxc6Q60/Dcb/dwMFOaozeZsnx14iFwracUXBjle1omlG8I+IdZlre0oFSOVVAqV4JSoomYqxWsbgwHt7CgVEE6GCvVaiFXEwaaiNtBqbFjx/LYY4+Rn2+5a5mXl8ff//53xo4d26iDE3XQSvcCo9U73C3toN8SmftJ2ZnSXusr5azZeWF9e0ppmVJ2+kGVF8HpHerzJDv9pDTx/dR/64pCywx2NZ1YC1VlVAUn8OFRte/SrUOTbFbx9TLwwrW9KSKAFVWmsgF7JXxallSnyywXhs4kX2kq4duPknWQRetSuPj53/l5b4b6O5m2GdDVnXVVw9K9Wfyr6hrLgsYoEWrMGfiKstQDuk4PMT25qF04Oh2k5BSTXVhe5+bWmVKA5Y9Cjf/H1iV8P1qV8B0+U0R6Xim+XnqGdaoxw5650blkSl2wIjqqpXNVpa5l/uWnqscQgw9Edq79urs9qsz9qZqgyTmoFzeY7jCWy0V0Qxw5coRHH32UTz75xOWbiOXl5RQUFNg8RCPQztfaDqr/PvR68wx8Pb3Um5H+3gaGd47kb5d34aMZg9n99Hh+nHUJT/+pJ1f1iefh8d24tGs0ZZVG/vrxdgqlOXrT0AJLviFgsD8pVC0XQvmevX5SYCnlawnXJ1XllhkCQTKlRNOxl8jQ0oJS2nljWLumyXZ3g9tBqVdffZVTp07Rvn17Ro8ezejRo+nQoQOZmZm8+uqrTTFG4Yi5ybkpO6KlzW7REpkzpexcQCWYylIzd9s0mTazaVhX3/I9O0GpUxtN/aTa1f5Dbk2vh86mwK+jEj7T8j3+gzEqOoZ2jKRbXO1eUAOTIrh5SDt+qh4MgNFedpN51r2rHY/JmlUJ329f/oenf9hPZkEZry8/gqKV9LUb6lbWjqIo/Lgng++Mw8mKulg9gbd3Ie2uxmx2rmUmBseDwYvQAG9zX6dtJ52fXOYWlZN6Vm2I3lcLSmm94cryoKLYZv2JfS2z8FWbSvh+P6gGp4d1isTfp8YflELpKXXB0xssmZ+ulNxpwabobvYvlsyZUi6W72U5ybpqDHq9KTCFlPA1QHV1NTfddBPPPPMMXbt2dXm7+fPnExoaan4kJtrpeSjcN3ouTHgZ+v+lYfsx/d+fN9yb7+8dzu6nx/HJ7Rdz/9iuXNIlutYstXq9jgVT+tEm1I+UnGIe/nI3irPJXUT9mPtJuZglBRdGppQW7AmtcZwIbUGNzs+mWLLYQDKlRNMptHNjOKSF9ZTSbs43RhVKA7kdlEpISGD37t289NJL9OjRgwEDBvDGG2+wZ88eOVlpbtZNzsFy560kR8oc7FEUywWUvfK9iI7qSUF1BWTauSDTIt4GHzUA4w7tIFR6tva/zYl16tf2Tkr3NFoJ35HltV9TFDjyCwD/zVabgU8bnuRwVw9fkcyugKFUKgb02Qch+7Dlxdxj6kWp3gu6Tah7XCZFnSYC0C7zV/Q68DboOHSmkOIdX6kr9LzGyda1Hcws5Hh2MQYvb/xv/xFu/831u47OaMG/xgjgavvQgsLgcgnfrrQ8ADpFBxLqb/pcfqHgYwok1ugPN6JzNMF+XmQVlpt7Vq0w9ZOqVbqnKFaZUhKUuqC5U3LnqJ+UeV+mgH1eat1BoIpiS2C3qcr3QPpKNYLCwkK2bt3Kvffei5eXF15eXsybN49du3bh5eXFH3/8YXc7LTNee9ib6EbUQ1giDLkTvP0bth/ThC0Rxcfp0zYMb0Pdp/URgT68dfNFeBt0/LQ3kw/Wun9zpqCskgc+38UN/95ATlHdGcGtjrtNzq3X1bY9H2lBp5oT9phvtuVDmYezLbV+Utp5UV6q45YYQjSEOSjVgjOltDYmTTFRjZvq1dEqMDCQO++8k7feeotXXnmFqVOn4u3dCBeKwj3FWqaUKSjlH67OnATSV8qe4mxTppPOnPJuQ6ezzHCXvq3261oQ0Lphnav8wtQSG7Bkr2hc6Sel6TRa7SGWfbD2LAk5RyAvlWq9N7+WJpMQ5s+Y7rH29wOE+nvz4NVDWGdUL06zN39heVHLkupwqct3+g5mFnD9yggqFAPd9Gl8ek0YV/dLoA05BGVvx61Z90yW7VEP2iO7RhPs14jHmMYsddX+r4VaglIDk9SgZV0z8Fn6SdUIcjpojO/jpWd8T/WP29I9GeSVVLDtpBr4Gl0zKFWWp5Z0We9PXJjMJXeuZEpp5XYOgkj+4RBi+v9hr9ectayDgKLOGBkU7dJQ68UclMpruve4wIWEhLBnzx527txpfsycOZNu3bqxc+dOhgwZYnc7X19fQkJCbB6iBdFaEWQddGuz/u3CefwqNQD9wk8HXZ6YA+BQZiFXv7mOr7ansfnEWV5ffrjujVobLave1SbncGGX7/kGq+fB4PlqDq2fVPvh6jWTUm1bzidEY9GSGVpyTyntxqLW1sSD6t1mff/+/fz88898//33Ng/RjMzle6aLAZ1Omp07o11gRXQAnwD76zhrdl7YgKwTnc4q0GB1IKoohtOm2f6c9ZPS+IdDouni4WiNbClT6d4ufS9K8OOWoe0x6J0Hz67oFcfJ2DEAFO740jKrm5ule7/uy+S6t9dzME/PVq/+AAwpWcNNQ9oxwbAJgKq2F7tV9qgoCktNvZMm9mnkTB/rVPKGli5o2Ux2MqX2nS6guLzK4abmflLtwmqMz/H/46v6aCV8mfxxMAujAt1ig2kbXuN3Wvs98wtr+N140bK5U3JnzpRyktnk6v6ytH01UT8pjWRK2VVUVGQOMAGkpKSwc+dOUlPVKdkfe+wxpk6dCoBer6dXr142j5iYGPz8/OjVqxeBgYGe+hiiIbSgVM5h1yY6sDJ1aHsm9W1DlVHhnv9tdynj6dsd6Ux+ax0pOcVEBfkA8OnmVI6cKXR76Be0+mRKXRDle+qxp1b5nvUyTwelco+qX6O6WmZzlhI+0RTMM2BbXftYXwu2hNLp87l87/jx4/Tt25devXpx1VVXMXnyZCZPnsw111zDNde4V5ojGqhm+R5YLmY9fdBvicxNzu2U7mmcNTvXIt5BjrOPnLLXV+rUJjBWqX+sw9q7th9HJXymoNSPpb3w9dIzZWDd5bQ6nY7x10+nStHTseo4361Yq2ZgZexUG3cnT3S6vaIovLXiKHd9vI3iimqGd46k7/hp6ov7vqV/YhjX+6k/y82BI137fCYHMgo5nlOMj5eey51kfNWLFkCqKGr4hW6B6f+aln0FtAnzJyHMn2qjYg481WS0eq2/1k+q5vjyawelhneKItTfm5yict74Xb3jVytLCqz6SUmW1AVPCyLVVXJXUWI5+XZUvme9v7rKAesqBWwsEpSya+vWrfTv35/+/dUbAXPmzKF///48+eSTgDorshagEheosHZqFnZ1udsTd+h0Ol64tjedY4I4U1DO3z7bYe5VWFNFlZEnv9vL7CU7Ka2s5pIuUfx6/0jG9YjFqMDzy+rIqmxt6lW+p2VKuTZzb4uU7yBTCqwy1D1cAmwOSnW2BKWk2blobIpif3IsLUBVWQzlLSCYf+48zpT629/+RocOHThz5gwBAQHs27eP1atXM3DgQFauXNkEQxQO1Wx0DlYHfcmUqiVba3LuLChlanZ+9ljtu1X1nXlPowUHrMv3rEv3XC0J7DJO/Xp8laU/VXkhnFwPwApjP67pn0B4oI9Lu4uLa0tWpDoD0PHVn1Kw42v1hfbDLdNW21FWWc3sJTt5+ZdDKIp613XRbYMJ7D1JnYUv+wC6o7/TvfoQRkXHa2nJbjVU1Ur3Rner3ay1wXwCLCeLDQ3g2smUAhhURwnf8ZxiCsuq8PPWk1yzGb32/9hOppRawqcG6U7mqk3Sa/WTAukn1ZoERFh+/5yV3GUfQC23i7b9u1GTu0EpexNHNKYLLCh16tQp0tIsx53Nmzcze/Zs3n33Xbf2M2rUKBRFqfVYtGgRAIsWLXJ6Xvb000+bs6zEeUpvgCi1hyTZ7pXwAQT6evHvv1xEgI+BdUdz7ZbiZeSXcsN/NrB4g1riNOuyziy6bTARgT48OiEZL72OFYeyWXskp0Ef5YKinT8GuNF/VDsnKS84P3scVZVbKgqcBqVaSPleZBeI7KQ+l0wp0djKC9TAE9hmSvkEgq/pnMbTJXyl5yznVedjUGrDhg3MmzeP6Oho9Ho9er2eESNGMH/+fO67776mGKNwpKhGTymw9AIpkEypWrJcCEoFRECE6Y9U+nbb1+xN7ekOLThgnSllbnLuQumeJranOpNDVSmcNAW1jq8CYyUnlVhSlDhuHZbk1tDiLp4CwOXKBk6v/wyAVd7D+HRzKj/vzWRzylmOZhWSU1ROVbWRMwVlTPnPBr7beRovvY7nrunFvKt7qU1W/cOg8+Xqjn9Qjwlb6c7WXB82p7iWlq4oCktNQakrezdRUKWxTpDMPaXa2iweaG52bv8z70hV74b2TgjFq2ZzWgc9pTRX9bFkP4X6e3NRzfI/sPyxk0yp1sGVkrvMOvpJmfel9ajaD0aj/XUUxbVSwMZwgQWlbrrpJlasWAFAZmYmY8eOZfPmzfz9739n3rx5Hh6dOO9o5zT1CEoBdI4JZv61vQF4c8VR/jDN6Aqw7mgOV/1zLTtP5RHi58UHtw7kgXHdzK0BOkYH8ZeL1Szv55YdcJhp1eqYg1JuZEr5hQKmm5PnY7aUdi7lHWD/c4e1gPK9krOWnl2RnSzn+5IpJRqbds3oG6oGoqxp15H2ZmRvTlo/qaA4x21tmpHbQanq6mqCgoIAiIqK4vRp9Qfavn17Dh061LijE85pQalA60wpx2U/rZqiWBqBOgtKgVWz8xolfPZmUXCHud+X6SBUUWxpqF6jyfnJ3GKue2c9T3+/r3ZPIp2udgmfqXTvj+p+DO4QSfd495rR6ntMQkFHP/1xkqsOYlR0PLinHY99vYeZH2/jhv9sYMxrqxn47G90nvsTI178g11p+YQFePPRjCHcPKRG6WGPyabPqv4enk4YD8D/NrtWSrI/o4CUnGJ8m6J0T9MYUxRXV1r+8NQISml9pXak5lFZXfvC3txPqmbpHjgt3wMY1imSsAC18fvIrtG1g1pg+T2ToFTroAWG7M0cqnG13C6yszrLaEWh4wawRWfUk3ud3jwDWJPxD1O/XiBBqb179zJ48GAAPv/8c3r16sX69ev53//+Z85yEsJl2v8/N5udW7u6XwJTh6p/x+9fsovU3BLeWnGUWz7YxNniCnrEh/DjrEvs/j2+7/IuBPt5cSCjgK+31y/gUF5VzccbT3Isu6jen6FFqU/5nt5gOdadj83OrftJ2cv8bwmZUlqWVEhbNVAQ2Vn9Pve458YkLkzmc3A7N9a1Zdr1g6e0oNI9qEdQqlevXuzevRuAIUOG8NJLL7Fu3TrmzZtHx44dG32AwoHKUig3naBbl2GESE8puwpOqz8vncHyR8gRc7PzGjPwWc++Vx8hNTKlTm0GY6X6b2Z1QKg2Kjzw+S62nTzHovUnmPDGmtoz42glfEd+BUVBMQWlVhr7cZubWVIABMWgaz/M/G1acB9GDujN5ckx9G8XRoeoQEL9LbPfVVYrdI0N4vt7RjC0k52Trm4T1ItaAHR0HXUTAD/tyeRscd1p6VqD89HdYhq/dE/TGCdIBacBRf2sAbaljl1iggj196akopoDGbWnQDb3k2pnJ73fSfkegLdBz02D1fT46we0tbtOgxrzi/OPObvJScmdq5lNBi9LA2VH+9MysiI7N30jfS1TqjSvad+nmVRWVuLr6wvAb7/9xp/+pM5KmpycTEZGC5mRR5w/ohuWKaWZe1V3+iaGkV9ayYQ3VvPyL4cwKvB/A9ry9d3DaBdp/056RKAPsy5Tz6te+fUQJRWOJ/ewx2g653n8273cuXjrhZFtVZ/Z96zXPx+bnZv7STnoZ9oYNwIbKtcUlIoyXQdo5Xv5pyztMIRoDM6qa4LttHPxBK0PYYTnm5wDuH219/jjj1NcrNZIPvvss0ycOJFLLrmEyMhIlixZ0ugDFA5oWVIGX8sJ+/+zd97hbZXnG76PJMt7zzhx7ExnbxISkpAQCIQZwqZA+QEtlLasUlpKJ9BCoUAKlFlGoRACZW9CQsiETIfsxFnO8N57SOf3x3eOhi3JWrZl57uvy5fko3OOPg+d8XzP+7xgP+jL7nvO6HlSyUPBFO55Xcewc1W1z/gEepNvy5TS9nNEK91rlyf1ytpDbDpSSbTZSEKUmYKKBi57fj0/nT2Yu84aTrjJCINPF7lNFQdh90cotYU0qmaOxEzgrFF+OotGXWQb08CZV/GPU8d3WKXNYqW6sZWapjYGJkW57+4XmQBDzoB9X0D2aYwaPpwx/YvZcbyGdzcf4yez3QvYqqra8qTODXbXPUeCIkrpeVKZYHDW+A0GhSnZiSzfU8LGw5WMG5Bge62xxcKeIhFw6Noppf2vNNdAUw1EdHS+/Wp+LjfNGkySu+ww6ZQ6udCFphKt5K7d/6Mot/OyfA+EyFX0gxClRrpoeFC8Szx2dZ4U9LnyvdGjR/Pcc89x3nnnsWzZMh544AEATpw4QXKyD84KiQTs3S9Ldgl3bXx/z+u7Idxk5JkfTeL8J1dT2dCK2WjgLxeN5spTslA6yby8bnoOr60/wrHKRl5cdYjbzxzm9fv+/Ys9fKJNRB0orefzHYWcP66Xn7f8cUrp61ccsG/fm9CdUq7ypMBhsu0EWNrE5Ed345gnBSJf0RwrXMGVhyFtRPePSdI3sd0zujiW6UJVTzulKnq5U+rss89m0aJFAAwePJhdu3ZRVlZGSUkJZ5xxRtAHKHFDfal4jEl3tsnqFyPNNX3mAj4o2PKkvDjhZIwRzpfGCru1sbXJXuPvb/c9mzJeJFo36yHnDnlS+SV1PPqlKIP9/fmj+PyOWVw6eQCqCs9/e5CLnl7LzhPVEB4LmrNJ/fI+ANZZR3PZ9OGuS7m8YeQFohRHMYjnLjAZDSTHhDMoJdq9IKUz625xY3v6PQBcPVWUBry5ocBj4PnOEzUcLm8QpXuuAryDRTBEKVvIuWu3ki1Xql2W1vbj1VisKmmx4fSLj+i4YXisPQjRTc250aC4F6RAOqVONpKHaSV3da5L7mpOQFOVcIumeFFu11lGVXd13oM+J0r9/e9/5/nnn2fOnDlcddVVjB8vJgA++ugjW1mfROI1iTmQPVN08v3+uYB21T8hkpeuP4VFE/vzv59N56qpAzsVpAAiwoz85hxxffX8qgOU1HjnOnlt/WGeXyVKp6YOEufLp1fkY+3Nbimr1X69GOWjU8rWga8XOqWqNAdUvBunVEw6GEygWqCuh27GbZ33NFFKUSBZduCTdAGeIl9cdWPvCXSnVGJoOKV8untta2vDZDKxY4fzRWpSUpJXJy1JENFLyWJSnZeboyEiQTyXuVJ2bHlSXszqm8IhY5x4fkwr4dN/38ZwiPShm4ojMWnihlC1iJvGdnlSFqvK3e9so7nNyqxhKVx5ShZxEWH847LxvHDtZJKjzewpqmXhv9byr2/ysQwVuVKKZoVezUSumupmhsob4jLhyjfhijc65CP5RdYp8LO1wtUFXDghk2izkUNl9aw/4H4WUA84P2NEGtFdVboHDlbyQJxS2rZuZqb1DnybjlQ4CXF5R8UF68SBCe6Pnfo+/Wla0NZiF66lU+rkoLOSO31ZyjAIcyGEtqezDnwluiglnVK+MmfOHMrKyigrK+Pll1+2Lf/pT3/Kc88FJipITlJm/FI8bn5VuGsDYNLARB6/YoKTu9cbzh/XjwlZCTS0WHjcRRe/9ny1s4g/fySOI3fPH86L104hJtzEnqJavt5d3MnWIUxTFahajuRJWb7n5jrUYLRfj/RUxIjNKeUQ45EkO/BJugBPzYZsTqkeLtcPsfI9n0Qpk8lEdnY2Foulq8Yj8RabKOXCtdNJHs1JSYlWapLqpTXXliulhZ071gb7K8AajPYD0a4PwdIi3FNJYpbmxdUHyTtaRWy4ib9fMs5JrJg/OoMv75zN2aPTabWoPPrlXm7f7CxImkfM9+yc8YbcBTDi3MD24YaYcBMLJwqh5Q03geeqqtrypM7rytI9sH9OajUruT/YnFKuRamxA+IxmwyU1bVwuLzBttwecu5B4Owk7Nwj+iyk0ex7+YCk9+IpV8qX0j3HfVUcFE0ZHLG0QqnW2ESW7/lMY2Mjzc3NJCaKz/+RI0dYvHgxe/fuJS2tC92hkr7LsPmQMly45Le81iNDUBSFP5wv8q3e3nSUPUXuxbGtBZXc9tZWrCpcNTWLn88dSnxUmC1s/akV+R4d1SGNLiiFx4HJx2uyXu2U6qR8DyBee60nRClLmzifgd0pBfZcKd1FJZEEgxoPTinHypmeoq3Z/jnsreV7v//977n33nupqOiFB8y+hJ4pFePiAjYUOlyEElar7zdQtg58mpsp0M57Onop1Q/viMec00BR2F9cy+NfiZnFP1wwisyEjsHBKTHhPHfNZB67bDyx4SY+ORHDUVX8/fdb+3PhnBkdtgk1rp4mLki+2llEWV1zh9d3nqihoKKBiDADZ3Rl6R6IrpWGMDGj6e9shS78unFKhZuMTNBmmzc6hNVvLagC3ORJ6dicUn7Yex1PhtLFevJgczdt7/iatyHnOjGpWmdXtWNXr/IDQlQ3x0BCtsvNg0ofE6UuuugiXntNCAdVVVVMmzaNxx57jIULF/Lss8/28OgkvRKDwe6W+u5ZIRz7S2MVbHtLOG59ZHJ2EueOzcCqwt8+cx28frisnhv/s4mmVitzc1N54KIxtkm4G2cOIjLMyPbj1azcV+r/z9CT2ELO/XDVR/VSp5SlzX6t4lGU0u9PeiDsvOqIaC5kinCOXNCdUrpgJZEEA5uZwYVTyrH7nrWHjD5VRwEVwqJFtloI4LMo9eSTT7J69WoyMzPJzc1l0qRJTl+SbsImSrlwSukOC+mUElQXQGu9cI0kedkhUhelCn8QF2aeuij4gm7j1EtfcmbSZrHyq3e20WIRF2iXueumhpiJvGTyAL64czanDU3hC4twdG2PmcmY/vFutwsVRmfGMz4rgVaLyjubOoqmetjpGSPSiDJ3cQimwWAXfvwVcPXt3GUoAFO0Ej49V6q4ponC6iYMCowb4OFvFhdA+Z7e0cPVyVDSd/FUcudPBpS7XCn9+JU2smOgelegi1KW5j7RIWnLli3MmjULgP/973+kp6dz5MgRXnvtNZ588skeHp2k1zL2ciEk1xyDne/7tw9VhbevhfdvhnX/9GsXvzlnBGFGhVX7Svm2nbBUXtfM9a9soKK+hbH943n66klOOZjJMeH8SJu8emr5/t7plvI35Bx6b/lezXERTWE0a5MZbtBFqaoeEKV0J1TSEOfzVrIs35MEGavVXrHg6r4xOk3k96oWqC/r3rHpVDqEnIfI5LXPd30LFy7sgmFIfEYv33OlbgZ6o93X0EPOU4Z73+0jabC4OGisEK4D28ElwJKy9rXF2TN5ftVBfjhWTVyEiYcWjfMqn61/QiSv3zCNN9f8gX9uGs3pC28MbFzdyI+mDmTb0SqWbCjg5tmDMWiB6aqq8ul2IaacN7abxJT4LFFT7e9npcZz+R7AKTlJwAE2HRE5UrpLanh6rOfMrEDK93SnVJwMOT+psJXcHYLmOgiPEd+3NUOZlvHirVNKX/fgNx1FLl9dV4FijgUUQBVuKW8ysUKYhoYGYmNjAfjqq69YtGgRBoOBU089lSNHXITUSyTeEBYB026GFQ/Auidh7GW+32zsfA8OrRLPt70lGpb4uI/s5Gium57DS2sO8bdPdzNzaApGg0Jji4WbXtvE4fIGBiRG8tL1U1yeA386ezCvfXeELQVVrD9QzoyhKb79DD1NIKJUby3f051P8QM8T1T0ZCWHnieVMtR5ue6Uqj0BLQ1gjurecUn6Hg1lovEEiuuKJqNJCFN1ReL/LtbPBlqBEGJ5UuCHKPWnP/2pK8Yh8RVPTqlgBDj3JWyd90Z6v42iCLdU/jIRdq47pfztvKfjKErFZLCnNZXFX68F4E8XjCbDVSc2NxgMCtfMHgWzuyHTJYicP74fD3yyi4KKBtYeKGPWMCGs7jhew9GKRiLDjMwd0U1W0kCs5C0N9otPDy24J2UnoihwqKye0tpmtjqEnHseWwCOR+mUOjmJSRXHqLpiKN1jz8Yr3Stm5CLiPQqoHXCXUVWsZfSldZMoZTBARJwQpJqqe+YCLogMHTqUDz74gIsvvpgvv/ySO++8E4CSkhLi4uJ6eHSSXs2UG2D1Y1C0HQ6uhCFzvd+2uRa0br6AcJac2Ar9fa+C+OUZQ/nf5mPsLa7lnU1HuWxKFre/tZWtBVXER4bx6v9NJS3W9fVOWlwEV52SxX/WH+HJFft7oSilCUq+dt4Du5DV25xS3uRJQc/en5TrIefDnJdHJYlzY1O1KOHL6IaOspK+jV7KGpMGxjDX68RmCFGqphAyJ3bf2HQqHJxSIUI3+O4lXYKnoHNZvueMLkp5G3Ku4xh2bsuUCtB54iASWLNncPf/fqDVonLmyDQWTfLhZrEXE2U22X7WN76zB55/ormkuqV0TyeQWTv9pBPm0PHS1VtEhpGbLlwRm49UkOdNnhTYMw+qj4uSCp/GJp1SJy2uSu4cS/d8cT047svxf7C7nVLQp3Kl/vjHP3L33XeTk5PD1KlTmT59OiBcUxMn9sDFqaTvEJUEE68Vz9c95du23z4irnUSc2DE+WLZ9nf8GkZClJnb5omb/8eW7eMPH+7gq13FmE0GXrxuCkPTYjxuf/PpQwgzKnx3sMIpj7FXEJTyPfcdikMSvRzPQ5QBAAk9KEqVaeV7Ke1EKUVxyJWSJXySIOBN5ItuUuipDnyVfUCUMhgMGI1Gt1+SbkBV7e3eXQadO5T99MZ6/GBTqjulfHQU9ddEqWOboFYTAYOVKQWsbM5lx/Ea4iPD+NvFY70q2+srXD1NhCMv211MSU1T93bdcyQgUUrPk+rf6Y2+KOGD7w5WsP24uKmeOLCTEFT9f6W13vcb8WCJqJLeh6tcKVvnPR9ngFNzQTGKFue6CNtULXL6ANK70aXZh0SpSy+9lIKCAjZt2sSXX35pWz5v3jyeeOKJHhyZpE8w/VaRV3JgORTt6Hx9EG7K754Rzxc8AhOvEc93vOt3EO+1p2aTnRxFaW0zb34vjhmPXz6eqYM6dxBlJkRyqZav+eTy/X69f49hCzr3xymlbdNUJXJpegv6OaGzxhf6pHlzdfcfy/VMqfZOKYBkraRP5kpJgoE31Qr69XmPiVKHxWMIle/5LEq9//77vPfee7avpUuX8tvf/pZ+/frxwgsvdMUYJe1pqYNWrb28K1EqNhNQRChsTwWohQpWC5RqWSppPjqldMt6xQG7ohywKGUXCR7aJSzp9180mrS43p2R4iu5GbFMzk7EYlV5e9NRfjhWzbFKrXQvtxtbogciSlV3nielo4edv7/1OA0tFmLCTQxJ9TxTjDnK3r3HV9ejLiC0zzCT9H104anIlVPKR2eTKVxk8TnuQ3eexvX3r7uUv+huxKaq7nvPLiQjI4OJEydy4sQJjh8Xn++pU6cyYoSP5ymJpD2JOTDqIvF8/dOdr6+q8NndIgNl+AIYfjYMmSc+33XFcOhbv4ZhNhn47Tn2/+f7zh3J+eO8Pyf97PShGA0Kq/eXkXe0yq8x9AiBlO/pQpZq7V3HOlv5XidOqfAY+3mjO91STTX2bNj2mVJgDzuXTilJMPDGKdWTopSq2kWpxF4sSl100UVOX5deeil//etfeeSRR/joo4+6YoyS9uh5UuYYMEd3fN1ktpf1+dO5qy9RcUiIc6ZISMjxbduoJHu3vjat41OgolRCNtac01kfNo391gzOHp3OheNPTuHg6qkie2DJhqN8vE0r3RuZRqS5Gx2XutXcn8+JLhR5yJPS0Z1S1Y2iTfe4AfEYDV444/QSPl1k8gZVlU6pkxlHp5TulPWn816H/e1w3pevztNA6UNOKavVyv333098fDzZ2dkMHDiQhIQEHnjgAay9yR0hCV1m/FI8bn+n82YZO98X4ebGcDjnIbHMZIbRF4vnP/hXwgdwzpgMfn12Ln++YBQ3zfLt5mdgchQLJ4jz69MrepFbKpDyPZNZa+wANFYGb0xdjV6+11mmFPRM2LnukopOs59LHNHL98oPdt+YJKFNQwWseNBeKeML+jW4p4lh3aRQ0wOiVF2JMLcohs5LbruRoGVKTZs2ja+//jpYu5N4wpYn5antagCdu/oSJVogb2quf63L9RI+AFOEx+wgrzAYWdz/H1xVezuJUWYeXHhyle05ct64fsRHhnG8qpHX1ouOU+eP7WYRRXc5NVWLmTRf0C+odOHIA5kJkfRPiLR932melG18mc7v5Q2NlQ4iqhSlTjpScsFgEuUR1cfExUd9CaD47haFjuWAPZEnBX1KlLrvvvt4+umnefjhh9m6dStbtmzhb3/7G0899RR/+MMfenp4kr5A/8mQPVO4n75/zv16zXX2cPNZdzmXcoy9XDzu/hhaG/0ahqIo/HzuUK4/bZBf1zo/nzsEgwJf7y5h54le8tkPxCkFEKU5iXpLrpTVar9G8eYG1xZ27keDGX8pd5MnpZOsTUBLp5REZ83jsOpR+Pbvvm+rC00enVLaaz3hlNKrf+IHCCE8RAiKKNXY2MhTTz3FgAGd35xJgoCnzns6MuxcULpHPPo7qz/AQZSKzfC9vXI7qhpaeHalODk+sHAMqbHhAe2vNxMRZrQFnrdYrESZjczpztI9cLaS+1wipzulvDvunZJjL3XqNE9Kx58OfPoJLjJJtAiXnFyYzEKYAiEg6SJS0mDXztrOaN+BTxf6pSjlN//5z3/497//zc9+9jPGjRvH+PHjufXWW3nxxRd59dVXe3p4kr6C7pba/Kr7SZdVj4r8k4RsOO1259eypkH8QGiphb2fd+lQ3TE4NcZW8vf0inyvtvnhWBVXvrCe0x5eQX5JbVcOzzWBOKXAIey8lwS81xWBtVXkD3ozEdYTTqkyvfOei9I9sDul6opFF0qJ5PgW7XGz79vayvc8ZUr1YNB5CHbeAz9EqcTERJKSkmxfiYmJxMbG8vLLL/Poo492xRgl7dFFqehU9+sE0uq+L6HfQPnjEABnp1RMgKV7wKbDlbRaVAanRPuUrdBX+dE0u9V73sj07i3d04nz8wKp2vvyPYApOfZZU++dUn44Hmu8sA1L+jaOJXeBOpv07cr2QWsTFOvHVFm+5y8VFRUus6NGjBhBRUUvuRGVhD7D5guBurkGtvyn4+ul++yZUwv+DmGRzq8bDDD2UvHczy58AFjaAmq68/O5Qkj4fEcR+4rdCwaF1Y3ctTSPC59ey3cHKzhe1cidS7fRaunGklir1R507q8opTusGnvJsUDPk4rvD0YvOifH90AHvnJNlHLnlIpMsP+9/Ag7b2hpY+PhClTZXKpvYLVC4Q/ieckuaGvxbXtb0Lmn7nuagNtY6bcT1W9CME8K/BClnnjiCaevJ598kk8++YQjR45w4YUXdsUYJe2xle95cErZRKmT3ClVEqBTKmMMGDVrY6B5UsCmIyIj4JQcP23dfYyhabHMGiYC33XXVLfjr4DrQ/kewKxhKZiNBkb1i/PeIaePzSenlH4ylKV7Jy2OJXeB5EmBEDcjEkC1wIEVoizQYLIHoHcXfUiUGj9+PE8/3TGA+umnn2bcuHE9MCJJn8RggBm/EM+/exYsrfbXVBU+/7UWbn4O5C5wvY9xWgnf/mX+OXcaKuDpyfDvM/0WpnIzYjlntLj++tc3Hd1SDS1tPLFsH3P/sZL3topz5UUTMomPDGP78WqvHVZBoblahJSDf933wC6O9JbyPT1PKt6LPCnoIaeUh857Okn+h50/+uVeLntuPR9t8yH/UxK6VBwUDlEAS4vd4OANbc32z66nyeGIBBELA3ZnVXdRGZpOKS8kbWeuv/76LhiGxCe8EaVk+Z5QtvXZkVQ/nVKmcMgYK+ybQbjJ33RYXNRNzunGrlUhztNXT+JQWb337qFg488FUlO1/YTlpVMqOzmaT2+bSXxUmPfvo5/QfPkc25xSUpQ6abGV3O2wX/T465RSFLG/I2tg+9tiWcrw7s8h6EOi1COPPMJ5553H119/zfTp01EUhXXr1nH06FE+++yznh6epC8x9nJY/oA4h+x83y4y7foQDq7Uws0fdr992khIHwvF22HXBzDlBt/ef+XDYla+8jAU5kHmRL9+jF+cMZQvdhbx8bYT3HHmcAalRGO1qry/9TiPfrmXohqRozglO5Hfnz+KCVkJfJh3nNvfyuPpb/KZNzKNcQMS/Hpvn9CFO3Os22NkU6uFsrpmBiRGud5Hbyvfq9Y773krSmlOqapuquSwWu2ZUu7K90B04Du2wa+w8+8Oir/VpsOVXDShhyZYJcGjMK/j95kTvNtWF5iMZs8dihVF3FdWHhIlfEnd6FrSnVLd+Z5e4LNT6pVXXuGddzraeN955x3+8x8X9mBJ8KkvFY8eg857YCYi1Kg4IGYBzbFe5/64ZMg88RhghkpTq4UfjokbKumUshMfGdZzghT491nRHYgRCT7l9AxLjyUt1oecJ8fyPW9nmW1OKVm+d9KiH6vK8+25eoEcv/Rt9VyZ7s6Tgj4lSp1++uns27ePiy++mKqqKioqKli0aBE7d+7klVde6enhSfoSYREw7WbxfO2T4jzSXAdf/k4sm3lH5zcm4y4Tj7524SvdCxv/bf9+j/+C65j+8cwbkYZVFW6pDYcqWPjMWn71zjaKapoYkBjJv66exDu3TLddT1w4PpPzxvXDYlW5c2keTa0Wv9/fa2wh565vRlVV5SevbWL2I9+w8bAb0am3lu8leNnFS7/mqj0hSju7mprj0NYoHL6J2e7X89Mp1Wqx2rLL8kvq/B2lJJSwiVJajvCJPDcrusCWJ+VFDrFudujuXClbplQvF6UefvhhUlJSOixPS0vjb3/7W1AGJekEb7rv6TeztYXdc9APRRzzpAIJKJ/9a7h5FUz4UUDD2XG8mhaLleRoMznJbmbIJN2PP6KUjyHnfqN/jtsavW8PLZ1SktgMUQKiWoX13Bwjgoz9RReh9K6O3Z0nBX1KlALIzMzkr3/9K++++y7vvfceDz74IJWVlXJyTxJ8ptwAYdHC7XRwJaz+hziHJQyEmXd2vv2YSwEFCtbZBQhv+PI+Ufarl6PtDcwF+IszhMvl3S3HuPz59fxwrJqYcBO/OWcEX991OueN6+fU4U9RFB68SDSUOVBaz6Nf7g3o/b2ik5DzZbuKWb2/DKsKL6855HoftvK93iJKaY4nb51SMelgCBPnp+64GdcrJhIHgdGDU13vwOdjptTB0npaLWLScL8UpfoGhdvE45C52vd53m/ry8Swfp1e042iVHOd1pGZkCvf81mUOnLkCIMGdVTWsrOzKSjw4WSl8cwzzzBo0CAiIiKYPHkyq1evdrvuypUrURSlw9eePXt8ft9eja37ngdRyvGgX9fNtaqhgi1PamRAu/n2YDXnvl3DjsLAOnLoeVJTchL9ao0s6SL8aU9sy5PqYpt2WAREaZMA3pbwSaeURFGc3Uxpo0S+jL+0z6PyN58qEPqYKCWRdBtRSTDpWvF82R9hnZZndo6LcHNXxPeHnJni+fb/efee+5dB/jJxHXr126IzW/EOe9mIH0wcmMisYSmoKhgUuGrqQL65ew4/mzOEiDDXTVISo838/ZKxALy05hDrD3RxTpMHUarVYuXhz+33K1/tKqZYKzt0Qi/56TWilB507qVTymCwRxN0RzWHniflLuRcx0+n1J4ie2fLsrpmKut9DMWWhBaqahelJl8vHot3eh927uiU6oyecEpVHRGPkYki4D+E8PkqNS0tjR9++KHD8m3btpGc7FuniaVLl3LHHXdw3333sXXrVmbNmsWCBQs6Fbf27t1LYWGh7WvYsE4ONH0Jq9VBlPKQKWUw2BXYkzXsXHdKpQYmSv3jy73sKqzhlbWHA9qPnic1JVuW7oUUtjDxE2D10t5f41vnvYCwXbx5+TmWTikJOAtHgZbbpY3AZmMHSJdOKYmkV3Hqz0AxQNEPYG0VnfnchZu7YqxWwueNKGVpFS4pEKWDA6bAwOnie70E2E/+cdl4bps3jM9un8VDi8Z61TTkjBHpXHmKEEzufmcbtU2tnWwRAHrJnYuQ8ze+O8LBsnpSYsyM7R+Pxary9kYXk2G9qXxPVe0Tet6W74HdVdUdopTulPKUJwUiUwqEsNhY5fXudxXWOH2fXyrdUr2aysPiOsNoFk0gIuKF47x0t3fb12gTw950wO4JUaoiNEPOwQ9R6sorr+S2227jm2++wWKxYLFYWLFiBbfffjtXXnmlT/t6/PHHufHGG7npppsYOXIkixcvJisri2effdbjdmlpaWRkZNi+jMYeaCPfUzRViQsKgOhUz+vaWt13U5hgqFGiHUACcEodKK1j+3FxE7R6f6nf7V6tVpXNDk4pSQgRmyFmca1t9tLYztAFoq52SoGDaObFxVtbMzSUiefSKXVy4yhEBSpKmaMhSSttiIjvnv/79kQkiEdLM7S6cBdIJBL3JObAqIvEc2M4LPi7b7EGoy4SN2klDh093bHpFSjbK9xCs38tlo04VzwGWMKXHhfBXWcNZ0RGnE/b/f78UQxIjOR4VSMPfOJDJy1fceOUqm5s5Z/LhThy51nDuWFmDgBLNhRgsba7ruxNQef1pVpZt+J1J2LA/67H/lCmiVKdOaXCY+2T/T64pfa0q6LYXyxFqV6N7pJKGyWaXfUbL773NlfKF6dUT5Tv6W7VEMuTAj+67z344IMcOXKEefPmYTKJza1WK9ddd51PmVItLS1s3ryZ3/72t07L58+fz7p16zxuO3HiRJqamhg1ahS///3vmTt3rtt1m5ubaW5utn1fU1Pjdt1uJW+JKKvzpp7fEd0lFZEgPiye0F0cJ2MHvtZGe8vLAPJPPsyzt3ctqW1mX3EduRmxPu/nYFkdlQ2thJsMjM6M93s8ki7AYBQ32dUFYtbOm9kNXSDy1q4eCI5h551h6/oRbp9tlZycOIlSQSi3Sx8tLtTTRgeW0ecv5hjh9FCtYhYzzIeGASHCokWLPL5eVVXVPQORnJzM+Z2INZh6k11k9pbIBOGu2vMJ/PA2nPUX1+s1VMBK7V5g7n328pDcBSJc/fBakY/oqSuVJ5rrROfA0ReD2ftszphwE49dNp4rX/yOtzcdY/6oDM4c5aHawF/ciFLPfJNPZUMrw9JiuGJKFm1Wlfs/3sWJ6ia+2VPiPBZ928YK4UTq5Hi7p6iG9NgIEqO7uSMq2POkYvv51pG1O0UpPSMq2YuqmqQhYnKy/CD0n+zV7vXyvUkDE9hSUCXDzns7uiili1H9JsChVVqu1I873153PXkzMdwTTqnKPuSUMpvNLF26lL179/LGG2/w3nvvceDAAV5++WXMZu8PSGVlZVgsFtLTnU8K6enpFBW5zkDq168fL7zwgi0YNDc3l3nz5rFq1Sq37/PQQw8RHx9v+8rK6oabyM6wtMHHt8PXf7Yr+N5iCzn34mRqO+ifhKJU2T5x8xKZ6Dl7ywOqqvJhnvjdRZmFG2/1/lK/9rXpsHBJTchKwGwKINtF0jX4eoFU3Y3lezZx+YTn9cDhZOhF1w9J3yZ1pHA1mWOCU26nl98MnBb4vvzBYIBwzR3RS0v4HK9FXH1lZ2dz3XXX9fQwJX2V1OHw8+/glJv8237c5eJx+/9ElIQrvn1EiE5po2CSww1c0mBxTFItIm/KXz6/Bz68Fb57xudNpw1O5qaZwh3w2/e2U9EV2T8uuu8drWiwxT/87tyRmIwGIsKMXDZF3I+88f0R533oE0qWFmjxLHCs2V/Ggn+u5vpXNvjt5A+Iar3znpch5zrd1SG8tdF+XdeZUwrsYedeOqUq6lsorhHGh/PGCRFif0lg+bOSHkYPNc+c4PzotVPK4Tq8MxxFqe76/Orle511Xe0BfHZK6QwbNiwoWU7tA59VVXUbAp2bm0tubq7t++nTp3P06FH+8Y9/MHv2bJfb3Hvvvdx1112272tqanpemKo9IUoQQNT3e3Og1PEm5Fwn7iR2StlCzkf5fXOed7SKI+UNRIYZueX0ITy+bB/f7ivlplk+zjDiHHIuCUF8uUBSVftnqjvKmHz5HPtSyy7p24RFwP99LnLSIoLgzpz6UzGzNnhO4Pvyl4h4UcLeS0WpV155paeHIJH4z7CzITxeOIUL1kPOac6vl+6DjS+K52f/FYztbjFGnCtyWfZ8ahe4fKGhwp5pdXSD79sDv5qfy8q9pewvqeP3H2znX1dPcnvP0Wqx8t3Bcj7fUcTq/aWcOTKdP5w3CoPBwzWlTZSyO6X+/sUeWixWZg5NYU6uPXbjqqkDeWHVQVbuK+VoRQNZSZrzKyxKuJ0tzWJ/4a7d+Varyt8+242qwrZj1Ww9WsWkgd18jamHnPuSJwXdJ0qVHwBUUV3ipiOiE3rYeXm+V7vXXVIDk6KYkCXOs9Ip1YtxDDl3dEqBKFu2tHru4Aj2igVfMqXamoSY3x0VDiFcvuezZePSSy/l4Ycf7rD80Ucf5bLLLvN6PykpKRiNxg6uqJKSkg7uKU+ceuqp7N/v3m0UHh5OXFyc01ePU+kwK1K0w7dtbU4pL0Sp7rTHhhq2kPMRfu9CL92bPzqdc8YIxXvDoQqaWr0Mw3ZAhpyHOL5cIDWUaxkKdI/4Yyvf82JsthkaGXIuQZTc9RsXnH0ZTeKm0oeSmaBjCzuv6rkxSCQnK2ERMOoC8Xz72x1f/+r3Iptx+Dkw5IyOr+eeJx7zl4v8Q1/ZtsR5QtcPIsKMPHHFBEwGhc+2F/HRNmcHcnObheW7i7n7nW2c8tevufalDbz5fQFHKxp5Ze1h7v9kl2dHUrug8y0FlXzyQyGKIlxSjgLYoJRoZg4V3QTf2ujQ4ElRvAo7/2jbCaeQ7Te/970DesDo5Xs+O6W6Kei83CFPypsJaj3svNw7p5SeJzUiI5ahqUI8LKxu6towfUnXUXNcXOMbTCKqAITLMzxeHHtKOgk7b6qxuxu9qWgKi7CXMte6rhILKlaLXUjuC+V73377Leedd16H5eecc47HMrr2mM1mJk+ezLJlzjbeZcuWMWPGDK/3s3XrVvr162U3YFUOolRngZHtqfei856OL1k0fY1S3SnlX8h5m8XKJz+Ii5WFE/ozLC2GjLgImtusbDzsW/hkaW0zh8sbUBS6fxZL4h2+iFL6OtFpnee6BQPH8r3O7L3SKSXpy8gOfBJJzzJWczjt/MC5RXr+17D/S3EzN/9B19tmToSYDGiphcOrfXtfVYVNL9u/ry2EOv/iFMb0j+e2eaJC4Q8f7OBQWT2fby/ktiVbmfzA19z4n038b/MxqhpaSY42c9XUgdx11nAAXl13mH98tdf9zh0ypVRV5a+fipvYSycNYFRmx0nxH00T4szSjcdoaXMoidRdPW7CzpvbLLZxnKXlUX3ywwmqG7tZDNFvcH3N19Sva5pruvZ4XqY5nrzJkwK7U6rigFflVLpTamS/OOKjwkjTukEeKK33eaiSEEAv0Usdac+tVBT75J5e2ucOXVgKj4PwGO/eU8+eqvUioiNQao6LZmlGc0jeJ/gsStXV1bnMjgoLC/M5RPyuu+7i3//+Ny+//DK7d+/mzjvvpKCggFtuuQUQpXeO+QqLFy/mgw8+YP/+/ezcuZN7772Xd999l1/84he+/hg9i6NTqthXp5QP5Xv6jXZD2cnXrUh3SvkpSq3JL6OsroWkaDMzh6WgKAqzhqUAsGqfbxdCm4+Ii4rhabHER3Vi+5T0DPoFlTeuwppuzJMCu+vJ0my/4HWHdEpJ+jLSKSWR9Cw5M8X5pakK8rVJZUsbfHmfeD71p+4jKQwGyD1HPN/jYxe+w6tFSZU5xt7lzU+3FMCtc4YwfkA8NU1tzP3HSn72xhY+2naCuuY20uPC+fH0bJb85FQ23HcmDy0ay23zhvHAQtEw4l/fHOBf37go71JVp/K9z3cUsflIJZFhRn41P7fj+sCZo9JJjQ2nrK6ZZbscuv/q7gk3otQb3xVwrLKRtNhw/nnlBIanx9DUauWDrd08CV3tp1PKHG3vMtiVbindKaU7oDpDbwDQVO1V98PdmlNqZD/hkhqWLoSI/cUyV6pX0r50T8fbXCldWPLlGlzPnuoOp5SeJ5UwUDR5CjF8FqXGjBnD0qVLOyx/6623GDXKtzDVK664gsWLF3P//fczYcIEVq1axWeffUZ2djYAhYWFFBTY7agtLS3cfffdjBs3jlmzZrFmzRo+/fTTTjvahByOTqma4761ffUl6DwyUdSm6+9zstBcZ5+9SfVPlNJL984f148wo/iYzBousgBW7y/zaV96yLnMkwphfHJKdWOeFAg3VrQmQnc2Pr2tbJwUpSR9kIgE8SidUhJJz2AwwphLxPMftBK+za8Id3pkIpx+j+ft9RK+vZ/7Fuy7SctjG3c5ZJ0ingcgSpmMBh67fAKRYeLGbEBiJD+ZNYh3fzaD9b+dx18uGsP0IckYHfKjrj01m/vOFdeUj365l5fXHHLeaVO1CHIHms3xPPy5cOz/ZPZgMuJddwsNMxq48hQXgeceyvdqmlp5aoUQW+48azhRZhNXTxWi0JvfF3Rf4LmqOmRK+ShKgf26q6oLI0bKHMr3vMEcZXeudBJ23maxsq9YL98TLrhhaUKckrlSvRR3opSeK+WtU8qbkHMd/Xq9phs68Nk674VenhT4EXT+hz/8gUsuuYQDBw5wxhmiZnz58uW8+eab/O9///N5ALfeeiu33nqry9deffVVp+/vuece7rmnkxNeb8DRKQXCLTXIdVB7B3SnVLQXTilFETfO5fvFzay3MwU9wfb/wfL74fL/CIt3AFQf2kw8UEoSkaZ4vDRQ2mhoaePLneLActEEu/Awc2gKigJ7imopqWkiLc67luQbZch56KNfHDVWClHTk+225pjzNt1BfH9Rultz3D5j4wrbLE3o2XIlkoCR5XsSSc8z7nJY/zTs+0KIEt/8TSyfe5/d4eOOQbMhLFqcq05shf6TOn+/uhLY/bF4PuUG2P8V7HwfCv0XpQCGpkazcfS7tFmtxF/5AooXzoGfzB5MfUsbi7/ez/2f7CLKbORKTRCyOZnNMby+sYiCigZSY8O5ebbn5jhXTh3Iv77JZ92Bcg6U1jEkNcbuInIxaf3CtwepbGhlcGo0l00W1yEXTxrAQ5/vYW9xLVsKKpncHfmljZX2/Bx/rofis4Sw2FW5t6pqDyz3tnwPxL1S7QmRK5U11e1qh8sbaG6zEhlmZKAWUj8kTXNKSVGqd9K+856Ofl9atMNz2Lk/ERrdWb6nh5yHYOc98MMpdeGFF/LBBx+Qn5/Prbfeyq9+9SuOHz/OihUryMnJ6YIh9kF0p5T+j+hLrpQv5XvgkEcT4k6pHe+K38sP7wS8q8M/iKyCLZbBvLDqoM/bL9tVTEOLhYFJUUwamGBbnhRtZmx/cVPkrVuqscXCzuPiBkqGnIcwEXEiyBA6/6zoTqnuFKXiHHKl3KGq0ikl6dtIUcqJVatWccEFF5CZmYmiKHzwwQce13/vvfc466yzSE1NJS4ujunTp/Pll192z2AlfYeMcZCSKxp+vH6xcPOkjoDJ/9f5tmERMHSeeL73c+/eb+t/RQ7KgFMgYyxkaC6Gou3+jV+nbB8xe94mYd//UHa+7/Vmt88bZhOa7n1/Ox/madcEmoBkiUziqRVCDPnVWcOJDvc8/98/IZIzRohreltYuZ4p1c4pVVLTxL/XiOvae84egUlz8sdHhnH+uExtH93U3Eh3SUWnQVik79t3dQe+uhKRWYViL8vzhmSHXCkP6HlSuRmxto6MwzRRSjqleiG1RaIaSTGIJjGOJA4SOVGWZntmsbt9gG9OqZ4o3wvBkHPwQ5QCOO+881i7di319fXk5+ezaNEi7rjjDiZPnhzs8fU9WpvsuS8jzhWP3nbgs1pEPhR4V74HDgf9EBel9N/JsY0B76qtQOxjm3UIL646SEmNb3laeuneRRMyO7QKtuVK7fcuVyrvaBVtVpX0uHAGJPpx0pZ0H952q6zp5vI9x/fydPHWWGnvTCQzpSR9ESlKOVFfX8/48eN5+umnvVp/1apVnHXWWXz22Wds3ryZuXPncsEFF7B169YuHqmkT6EoME7rtq07Uc7+q+jQ6Q252rXvXi9ypaxW2PyqeK6LXnrocHm+cDb7i+P15ooHhQPCCxRF4bcLRnDNqQNRVbjr7W18saPIJiAVt0ZR3djKiIxYLpviXQD4j6aJ2JL/bT4mOjzr5XvtciQXL99PU6uVSQMTOHu0833A1Vpo+ic/nKC6oRsCz215Uj6GnOvo23WVKKXnSSUMtIdWe0OSdx349rTLkwK7KHW0soHGFt87dUt6EL10L2W4yDxzxGCwl/R5ypWy5br64JTSXVWeJp2DRYiX7/klSgGsWLGCa665hszMTJ5++mnOPfdcNm3aFMyx9U30g3hYtL1kr9jL2Z76MlCtQsWNTvFuGz0QsqYLgwSDga4QF25z7ujiI20WKxl1wnl2OHIkja0Wnvh6n9fbV9S32ILMHUv3dGYNE7lSa/aXYbV2Xrevh5xPyUnqIHBJQgxvZ+2qe6h8Dzy7uPQTWlRy93QFlEi6GylKObFgwQIefPBBr3M1Fy9ezD333MMpp5zCsGHD+Nvf/sawYcP4+OOPu3ikkj7H2Mvsz4fNh6Fner/t8LNBMYroCr2cxB0HVwgXfUQ8jL5YLItJE138UH3vYO2IoyhVeQi2vu71poqicP+FY7hk0gAsVpVfLtnC7gPihi+/TjSD+t25I50yqTwxe3gq/RMiqW5s5dMfCl2W7x0orWPpRnEP8dsFIztcU04amMCIjFia26y8t7Ubrvn1LCh/8qSg651SvuZJ6fjolNLzpACSY8JJijajquLvJelF6GKTnh/VHl2U8pQrZROlQtQppR9v+4JT6tixYzz44IMMHjyYq666isTERFpbW3n33Xd58MEHmTgxsCygkwI9TyoxG9JFJw9K9ojuJZ2hh5xHJXufmh/vhcOip7Fa7D+bpdl7kc4F2/fl059SrCjccOlCAJZuPOp1J4xPfzhBm1VlTP84hqZ1zBWaNDCRaLOR8voWdhV23m1yox5yni3zpEIeby6QrBaHmvGecEp5EKX8maGRSHoTUpQKKlarldraWpKSZGm5xEcSc2DE+SJDav5ffds2KgkGThfPOyvh0wPOx18tQqh1dLdUAGHnHNssHvUJ4m8fgdZGrzc3GBT+fslYzhvbj1aLyofrxLVrhRrD7OGpzNaa43iD0aDYnE5vfH/EZdD5o1/sxWJVOXNkGlMHdfzMKorCVVq+1ZIN3RB4rpfvxfvplIrvaqeUH3lS4OCUOugxjN/eeS/OafnQVFnC1ytxF3Kuo+dKeXRKacKSP5lS9SXeaQH+0lBhv3bq7aLUueeey6hRo9i1axdPPfUUJ06c4KmnnurKsfVNqg6Lx4RsYZ8zxwghRj94eqJez5PysnQPekf5nu4A09EvFPzg4LZVAJSYBzJlxCDOHp2OVYW/f+GhBtiBD7TSvYUuXFIAZpOB6UNErX9nJXwWq8qWAiFKnZIjL/pDHm9Eqbpi0V1HMfo2ExIo+tg8OqX0skJZuifpo0hRKqg89thj1NfXc/nll7tdp7m5mZqaGqcviQSAK/4Lv9oLqcN931aPr9jzqft1ak7YRasp7fKqMsaKR39FqeY6KNFcVhc+BfEDxcTOhhd82o3JaOCJKyZwxog0xiCcOZVqnK1Lny9cNmUAJoPCloIqDjVo5WYN4hpyS0ElX+wswqDAPeeMcLuPhRP7ExFmYF9xHZu1JjtdRnWQnFK1J7wunfQJm1NqqG/bJeYACrTUQr3r6/zqxlaOVwkBMzcj1um1oelSlOqVdCZK6Q6q4h2uxSOr1T+nVHSquKdQrXaDRlegu6RiMpwF/hDCa1Hqq6++4qabbuIvf/kL5513Hkajl04diTOOTimDAdJGie+LvciV8jXkHBzK90JYlKpt1wYzgFyp5sNi2+Z0oWjfc84IjAaFr3eX8N3Bck+bcrSigc1HKlEUuGC8e5VbL+Fbvc9z2Pm+4lpqm9qIMhsZ0e6kJQlBvJm108Xd2H7euxWDgWPNudXqeh095FzmSUn6KlKUChpLlizhz3/+M0uXLiUtzf01xUMPPUR8fLztKyvLT1eEpO+hKP6XiucuEI9H1ok8RFdseV1MAmWfBqm5zq9laE4pfzvwndgqbgLjBggRYs5vxfI1T/h8fDGbDDw3qYDzjd9jURUaR1zcQajwhrTYCM4eLW5m39/bIBY2lKOqKg9/JiZWL508gOHp7vftFHi+ocDnMfiE3rTJX1EqOg0MYeLv0P4+IBj465QKi7BfD7oxDOwtEi6p/gmRxEc6d2IbZuvA512FhiQEqC+zx9zoLsz2JA0Gc6xo8OAq7LyhHKxtgOKbecRg6J4SPlueVE7XvUeAeC1KrV69mtraWqZMmcK0adN4+umnKS31LuxZ4oDtIC5CDW0J/16JUpqC6pNTSnP8NNeE7oW87UOo1ccf9y+brLimif71uwBIzhXW8CGpMVw1VZxcHvpst0c7s95BZcaQZNLj3Ici6mHnm45U0NDi3mq5SZulmjQw0dYhRRLCeBN0rp+04ruxdA80oUkRHYjczNzZ2sn6YhuWSHoTjqJUV5em9GGWLl3KjTfeyNtvv82ZZ3rOArr33nuprq62fR092k2dvSR9m6TBkDpSiE77l3V83dIGW/4jnk+5oePr+o1jyS7/XDb65OeAKeJx/JWio2BjJazzrnGAjZoTmD+7C4Cjo2/hxisv62QD9/zoVCHwvLOzXixorefb7YfYcLiCcJOBO87s3JWmlwF++kNh1wae65lS/pbvGQxdFzHS1mJ3hviaKQWQrHXrcxN2bs+T6igQDksTy/ZLp1TvQc+JSh4K4W5EX8ewc1e5Uvo1eHQqGMM6vu4JfTK5tgvDzvXOe0mhGXIOPohS06dP58UXX6SwsJCbb76Zt956i/79+2O1Wlm2bBm1tVIR9gpHpxRAhpYr5U0HPt0pFe19nTrmaIhIEM9DtYRPnyHJmiYeKw5CvWdXkyu+3VPCeIM4gcQMnmZbfvu84USZjWw7Vs2n213PxqiqaivdcxVw7siglGgGJEbSalH5/mCF2/U2HxavTZZ5Ur0Dx1JXd24k/TPUnXlSIE5w+kyKu6YF0ikl6evoopSlRcxWSnxmyZIlXH/99bz55pucd955na4fHh5OXFyc05dEEhQ8lfDlLxMO/6hkGHlBx9cTcrQW7S1Qutf39z6mTX4OOEU8Goxwxu/F8/X/gjovJ92tVvjgVmiqgn4TyLnkAcJN/ruopw9OZnBKNIUtEdRHiHP5iU8fAuD603LITOi8i/PELHvg+btbuiivqalG/Mzgf/c96LpcqcrDQvA0x/h3TZTkOexcz5Ma0a+jgKHn0R4pb6C5TXbg6xV0VrqnkzlBPLrKldINFv5Ee3SLU+qweOwLTimdqKgobrjhBtasWcP27dv51a9+xcMPP0xaWhoXXnhhV4yxb9HBKaXVxftUvueDUwrsB/1gl/Dt/UJYoANF/xCmjRCtOAGO+54rtXPnVhKUetoUsz1EHkiNDefm2eIE88gXe2lp6yg47CqsIb+kDrPJwDljPB9QFEWxlfB9u8/9hYseci7zpHoJsf1EZ0trqz2/rT36Z6i7nVJgF8LctY3VxV3plJL0Vcwx4jMKoev87Ubq6urIy8sjLy8PgEOHDpGXl0dBgSjbuffee7nuuuts6y9ZsoTrrruOxx57jFNPPZWioiKKioqorpa/S0kPkKuJovlfQ1uz82ubXhaPE37kukTQYLBf5/maK6WqDk6pU+zLR14AmZOgtR5WP+bdvja+CAe/AVMkLHrRd4dEOxRFDzxXeNwocrQua3qXyREnuPV077KR7PvowsBz3VEemejeWeINNlEqyA7Mci1PKnmIKDNth6qqvLWhgDX73cRw6B34OnVKdRTp0+PCiQ03YbGqHC5r8H3sku6ns857OvrrrpxSNQFUKzhGdHQVNlGqDzilXJGbm8sjjzzCsWPHWLJkSbDG1HdpqrHXzutOqXQtU6q2sHN3kD/le9A19tiiHbDkClh6beD70m6mvzlupEnLgvI1V6rVYrXlSTWljulwYXDTrEGkxoZTUNEgOpu040PNJXXmyDTiIjq/qJitlfCtdhN2XljdyPGqRgwKTBiY4MuPIukpjCZ7Fwx3nxV9ub929UDQT1ruHI/6yUw6pSR9FYNBuCNAilLApk2bmDhxoq3z8V133cXEiRP54x//CEBhYaFNoAJ4/vnnaWtr4+c//zn9+vWzfd1+++09Mn7JSU7mRBG621IHh1bbl1cV2Ev6Jl/vfntbBz4fOzZXFYiJJ0OYc36MosA88dlh00v27nLuKNkDy7T15z/gX+C7Cy6dPACzycBL5WP4Wp1KmGLhmbhXiY/w/pZt4cT+RIYZ2V9SZ4uSCCpVAYac63jTYMYf9JDzZNdC3voD5fz2ve3c/PommlpduJlsTqmDHV6yWlVbplT7znsgREEZdt7L8NUpVeQi7Dwgp5RevtcF2Wo6uijVF8r3PGE0Glm4cCEfffRRMHbXd9FdUpFJ9pmF8Fi7la4zt5Q/Qefg0E4+iAf9/V9p+zwKLQHOBGhi25dHFb6s0m72fRSlthypJNeyD4CoQVM7vB4dbuJOrRb/yeX7qWmy19lbrCofaaLUheO9c8DMGJKCQYEDpfW2DhyObNJcUqMy44gJN/n0s0h6kM5ypfTPUHeX74FDBz4Xn+PWJnvraOmUkvRlZNi5jTlz5qCqaoevV199FYBXX32VlStX2tZfuXKlx/Ulkm7FYIDcc8TzvZ/Zl2/+D6DC4Dl2x4or/A07168vM8ZCWLtyuMFzIGeWKAtc+Xf3+2hrgfd+IsqIh8yDU27ybQweSIgyc/44cZN6X/N11BNJes0Ou3vMC+IiwrhgvNjHm993QeC5LtgFOkGnX9dUdZVTynWe1NubxPvVt1hY7cotlewgSrVzmhVUNNDQYiHcZCAn2XUXMxl23otorLTfn7sLOddJGqKFnTdCWbuyYT0PKtaPa/CuFqXamu33L32pfE8SAO3zpHR0C3KnopTulPJRlNKdUsEs3zuwwv480A+Rtn2xmshrR4UDieNb3Of6uOCbvaVM0PKkDI52bAcunzKAIanRVDa08txKuyX3+0PlFNU0ERdhYu4I7/K64qPCmJCVAMAaF24pvRXvlGxZutercMyVckWolu/pn0FThLDTSyR9FSlKSSR9B72Eb+/n4ubf0gpbXxfLXAWcO+LolPKlRK19npQjigLz/iSeb3vTfV7Vtw+LssHIRLjoXy5LxALhR9PEfUIxSewd+yux8Ou/+JQNe9VULfB8eyFVDS3QHESBpFoTpRKyPa/XGV3mlNK65rkIOa9ubOXzHfbsni92uMjxScgGxQitDR3ucfTSveHpsW6bGA21iVLSKRXy6C6pxJzOr58NBvtxp32uVCBOqThNlKrpIlGqqgBQISzat1zqbkaKUt1J+zwpnQw9V2qn+23bmu2hgv5mSgXroN9cBwXf2b8PcL+q9kEuURPJa+lPmyECmqvtMx1esGbPcUYph8U3/Se5XMdkNPDbBSMBeGnNIQqrhcPpw63iJv/csf18CqjUc6VW7es4y7JRCzmfkiMFgl6Fpwuktha7WzFuQPeNScdWhuviorTWIeQ8yBfHEklIIUUpiaTvMGi2uFGqPSEySvd+JiZgY9Ih91zP26bkgtEsrhf10hRvcJUn5UjWKUIsU62w4sGOrxd8B2ueEM8v+Kf9hjKITBqYwPUzcrh8ygDGL7wLBkyFllr4/B6v9zEhK4GR/eJoabNw5O3fwMMD4avfB2eAulMqkJBzsJf/VR/tVFhUVdX74PBy9+V7H287QXOb1VbF8PXuYlot7SbBTWb72NrlStlCzl103tPRO/DlF0tRKuTxtnRPx12uVCC5rjanVBcFnTuGnIfwPYIUpboTt06p0eLRU1283gbeEGbvpuctcUF2Sh1ZK8KgdQLZr6XNdqNfoiZgwcguRWvFqs9mdUJhdSNKyS7ClTaskUkeQ9zOHJnG1JwkmtusPPbVPppaLXy2QxxIOuu6157Zw4Wra01+GRar/WRa19zG7kIxkyKdUr0MT+V7tScAFYzhEJ3SrcMCPH+OAwlYlEh6EzZRqqpHhyGRSIJAWAQMPUM83/uZvURt4rWdh4abzJA6Qjz3NleqrdkejD5givv1zvg9oMDuj4RzX6epBt77qRCsxl8Noy7y7n19RFEU/nzhaB65dDxGo1GIXwYT7PkEdn/s9T6unprFfaY3GH/4ZTHmdU/BltcCH6Bebhdo+Z5+XdNS1+lEw0Of72H0H79k/YFO8ncbKqBBW8eFKPWOVrp327yhJEebqW5s5buDLvZpK+FzFqVsIecu8qR0dKfUobJ62toLXpLQwldRyl0HPlsH7AAypZqroaXe9+07o+KQeAzhPCmQolT34s4ppZfvle4R1mVXOJbuGXz8szk6LILRhcOxdA8CE6XqS1BQaVMNRCemE202sq5ZF6W8y5X6dm8pEwzCqmvoP9mjCqwoCveeKy5i3t1yjGdXHqC2qY1+8RFMG+SbgDR+QAKxESaqG1vZftx+Mt1aUIlVhQGJkWTER/i0T0kP48kppTuU4jJ7ZqbBsXzP2m620NEpJZH0ZSITxKN0SkkkfQO9hG/rf+HgSkCByT/2bltbCZ+XuVKFP4i8qKgUz9kq6aNg3BXi+fL77cu/uFdcy8cPhAUPe/eewSB9FJx2h3j+2a+9O/6pKpeXP8NPTCKvqypztlj+yV3O1Q6+0lhlF2oCDTo3R0FUsnjuoepi+7FqXlx9kDaryhPL9nneZ7lWuhebCeExTi/tLapl27FqTAaFSyYNYP5oUXnisoRPDzsvby9KaSHnHpxS/RMiiQwz0mKxUlAhO/CFNN523tPR1yvabg87b2uBBq1qxp9MqYg40V0YuqaEz9EpFcJIUao7ceeUSsgWwWmWFvvBtD162ZA/taCxmYAClmaod9P+1Bd0USpN6xzoQ417B7Sb6VISGD0giYsn9SfPqs1sHPfOKfXN3hJbnhT9J3e6/sSBiZw3rh+qCv9cLiy+F47PxGDwTWgwGQ2cNkQ4Zlbts+dK6SHnU7Jl6V6vw5MoZcuT6oHSPRCzL4oRVIv9eKCjn8S6oIxAIgkpdKewFKUkkr7B8LPFuU2fXBk233uxI0NzN3gbdu5YutfZ5NLce0V1wsFv4NAq4VDK+y+gwKLn7a7N7mL2r4VQUlvoLJS5QlXhs18Tvul5AO5tvZE/x/4FRi0UlQ5Lr/EvXLyxCl6/WBx/YzJcZjb5TCe5Ularyp8+2mGbU99wuMKW2+oSvfNeinuX1LyRaSTHhHPOGHHN9OXOYqeKB8DulHIQpeqb2zhSLkQmT04pg0FhSFo0IHOlQpqmGrvA6q1TKnmoEJDaGqFME0jrNFHTEAZRflbI6A6rrgg7r9ScUlKUkgDiBGFzSuU4v2YwiFkQEG0mXWHrvNcxT6rNYuWz7YXUNrlxWZnM9u1cde7yhaqj4kOoGGD8Vdo+AxGlhAOsWE1gcGo0156aw1ZNlFKLd3ZqY2xps7I2v5zxiveiFMA9Z+cSZrRfkPhauqczSyvhW+0Qdr7piJ4nJUv3eh36xVFDGbS266rYk533AAxG+0mr/WcukK4fEklvQmZKSSR9i6gkGDjd/v2U//N+W1+dUjZRykPpnk5iDky+Xjz/8nfw8e3i+Wm3Q/YM78cYLMIi4ILF4vnGl6Dge9frWa3w6V2w8UVA4eisR1himcdnO4upPGuxyLGtL4W3rvate7YuSJ3YItxN17zbsXuhP9hyb12LZB/kHWdLQRVRZiPzRohGT89/e8DluoDbznstbVbe3yqunS6fIt5z+uBkYiNMlNU1s6WgndCV1LF8b2+xcEmlx4WTFG32+GPZcqWkKBW66GW/cQO8j+UwGBw6f+aJR1vIeQC5rl3ZgU+W70mcqC8TXRxQXAcD2jrwuamLt4lSHTvvvbXxKLe+sYXfvOvhpOwpJNkXdJdU/yl2Ic1VNzBv0T58JWoig1Ojyc2IJXvQUArVJBTV2rFmtx2bjlRgaK5mqEEbg5eiVHZytK27yfD0GEb2c2/D9cRsLex8S0EVtU2ttFmsbC2oAmTIea8kIsFuoW3/WelppxTYBbH2M4rSKSU5WZCilETS98hdIB7jBginlLekjwYUcS1Z17ETcgc8dd5zxexfQ1iUuHltKIf0sTD3Pu/HF2wGzYYJ1wCqEMnaWpxft1rhk9u1bC4FFj7DgDN+yqh+cbS0WXlrWwVc+aYoXyz6AT78uXexHo1V8N9FQpCKTILrPoKMMcH5mTxkedY2tfLQ53sA+OUZw2zxG8t2F7sXe2xOKWdRasWeEsrrW0iNDef04eLa3WwycOZINyV8yVqUSMUhWzdwPS92RIZ7l5SOnislRakQRheV9Jwob2mfK2XLdQ3gGryrRClVdSjfk6KUBOwuqdh+YArv+Lp+cHfXgc+WKdXRKbVeC+j7YkeRraNcB9zdzPqKLkoNOSM4+9TU5WI1kcEp4gB+3fRsm1uqrWCDx82/3VvKWMNB8U1iDkQne/3Wv5o/nJtPH8zfLxmH4qeynZUUxaCUaCxWlfUHytlTVEtDi4XYCBPD0/wTuiQ9iKK4v0DSRar4HnJKOb63dEpJTlakKCWR9D0mXw9TboSF/xKuYG8Jj4UkTTzozC1VWwzVBYDitktzB2LTYdot4rkxHC55UVQf9CTzHxCiUuluWPdP+3KrBT76pQgyVwxw8fMw4WoUReH603IAeHL5fo5YkuGK10Vw+s73YPVjnt+vqVoIUsc3C0Hqxx8HT5ACe86uLiY58NSKfEprmxmUEs0NM3MYmhbLWaPSUVV4cdVB1/vTY1DaOaX+t1lc010yaQAmo/329+zRwoH+xY4iVEeBLn6g+B1Zmm1VJnv0znteTGQP00Sp/SW1na4r6SF8DTnXad+Bz+aU8iPkXEcXtIKdKVVXLEoNFUPgjQm6GClKdRc2lTLb9eu6U8pt+Z5D0Hk7th2tAsCqwpLvC1xvr99oB1K+Z7VoIZTA0Hl2Uaqpyu9uAS1V4ua6RCvfA5g/KoP9YbkAlO5Z63H7b/aW+Fy6pxMbEca9C0YycWBgjqZZw7Rcqf2lbDwsSvcmZyf6nFElCRHc5RvYyvdCwCnl6E5UVfsJUTqlJH0dKUpJJH2P8Bg4/3EYPMf3bb0t4dNzStNGCTHLW2bdJQSzS1+GtJG+jy/YRCXBOVrI+rePQlm+uD7/8Oci80oxwqIXYfwVtk0unTSA6YOTaWy18Ot3fsCaNR3O/Yd4ccWDsOcz1+/VVC1K9rpKkAK7a61gvc2RBMJh9PIaUXb0x/NHEW4SYuUtpwsR8v2txymuaXLeV3OdXZRKzbUtLqlp4pu9wkl32RTna7jTh6cSGWbkeFUjO47X2F8wmuwZPFqulN55b6QXTqlh6fbyPWv7vCpJaOCvKKU7pYq2i89eMCaG9W1rA6g+coWuP8QP6HlBvROkKNVduOu8p5M2ClBEWJqrMPJ6zZbcTpQqq2vmWKXdHbVk41FaXbUftd1oB1C+d2KrEKDC4yFzktYtQDux+1nC11QhxtMQkUpshGj/azYZyBg1C4Dwos1urcXHqxrZV1zHRB9CzruCWVoJ3+r9ZWzSwhdPkXlSvRd3opQu6PakU8qVO7GhXDRJABE8KpH0ZaQoJZFIHLHlu3QiSvmSJ+VIeKwQzEae7/vYuoqxl8KQecLF8/Ht8P4tsG2JEKQufUm87oDBoPDIpeOINhvZcLiCV9YdFtldp9wEqPDeT6Bkt/N7dBCkgliy50i/8aJEsrFSdCEHVFXlLx/vpM2qMm9EGnNH2O99JmcncUpOIi0WKy+vPeS8r6Pfg7VNOEIcolLe23oci1VlcnYiQ1KdO/JFmo3MyRXX8V/sbOdScciVUlXVJ6dUVmIkZqOBplYrx6vcVLFIeo6WentQubed93SSh0JYtIjlKdsXHKeULejcRSfIQNDzpEI85BykKNV9uOu8pxMeYw8gK3bhlnJTvqe7pAalRJMSE05pbTNf7SzuuH2cm7IfX9BL9wbPFjMI4JBV5Z8Dy6rZFE1xzury6XPOok01kGStIP/AXpfbrtxbAqhMCdMsvP19vNAIEqcOTsJkUDhS3sA3e0T212TZea/34kqUamkQF0zQc0Hn4Lp8TxeEo1NDfhZEIgkYKUpJJBJHMrx0SvmaJxXKKIoQykyRcGQNbH9blJpd9iqMvtjlJllJUdx3nsiCfeSLPRworROOq5xZ0FIHS66CBuH2dy1Ije2an8Vktv9NjojqiK92FbN6fxlmo4E/nD+qwya3nC7Eoje/K6DGscmTtj05M22LVFXlba3r3uVTXDvdzxljL+FzwtaB7yDHqxqpbW4jzKjY4kY8/lhGg60CxJYrteFFePcmkdEl6VmKdoBqFZO5sR2jcTxiMNodmifyHDKlAnBK6dsGu3zP1nkvtPOkQIpS3UdnTinQAhtxXcLnpvteniZKTc5O5KqpYlbg9e8Od9y+k5arXmHLk5pnX+aqnMgHwhqFAyw61flEkZGSxIkIYdHdsPorl9uu3FtKPypItFaK2SH9ANHNxEaEMUkToRpaLJgMCuMHJPTIWCRBwFUnGF0EMsd0fxtoR+JcOB71UMRYWbonOQnQP3+NVd4F9Eokkr6Nfu1XfkCUb7nC0gbHt4jnfUGUAuF8mPs78dwQBpe/BqMu9LjJVVOzmDUsheY2K3e/sw2LYoLL/gMJA8XN6zvXC2HqdT1DKrFrBSkdXUQ6so6mVgsPfLILgJ/MHkROSnSH1efmpjEsLYba5jbedIwtObxGPGafZlu0paCKg6X1RIYZOW+ca9HgjBFpmI0GDpTWs7/YIQNKzyurOGBzSQ1Ni8Vs8u72eWj7XKkVD8L2d+Dt68DipmO6pHvQ86B8Ld3TccyVCqpTqjC41za2+KCc4O2zi5CiVHfRmVMKRFcP6OiUaqkXsxgg3BAO6KLU+KwErpo6EIMC3x2scD6ogl08qi0UJ2dfaaqGo1ro+JC5DvvVlV0/HFiWVqJbxaxMUvrADi+bs6cB0Hz4e+eZEKC5zcLa/DLG66V76aOD05rWT2YPs7cSHdM/nkizD0GdktDClYBry5Pq73+712CgO6Xqiuyf42DM0EgkvQVdlLK2QqssiZBITnpi0rTSddV9s6DS3dBaD+FxkDK8W4fXpUz/OVz4FNzwJYw4r9PVFUXh75eMIzbcxNaCKl5cfVA0CLrqLVGOdOhbeHKiyN+KTNQypDoKUqv2lfLFjiA6OrJniMcj63jh2wMcq2wkIy6Cn88d6nJ1g0HhZs0t9fKaQzS3WcS9ki48Ojil3tFcUueO7UdMuMnl/mIjwjhtqGiU5OSW0itYKo845El5n0c2TGt4tL+4Trjtm6rEC4e+hU/uDEx8qC+H5fd32qVc4gY9T8rXzns6jh34bKJUANfhevyGtVXEcgQLvXwvSTqlJCBC0PSbWk9OKVsHvnailO6SMkU6hTOqqmor35uYlUBmQqSttel/vzvivI+YdDGTolrFDa2vHFoNqkXUVyfmoKoqH287QSGaGOOPA0srSWxRjfTP7GipTR8pZjpGq/t5b7Pz/jcdrqShxcKMCO3D5mtGQJDRc6UApsjSvd6NoyilXzDoomtP5kmBEKUNJufPsXRKSU4mzDGiiwzIEj6JRCLoLOxcz5PqPwkMfejWx2CESdfBAO8zVTMTIvnDBaIk7vGv9olJ7PTRsOh5sUJTlRCkruvokLJYVR7+fA/XvbyBW/67hSPl/jU56kD/yWA0Q10Rn3wrSvB+d95IosyuRSSAC8dn0i8+gpLaZj7YelxMnFtbxeSh5gppaGnj421i4s5d6Z6OrYRvp8M9kl7yVHmY3SeEKOVNnpSO3SlVZzcnmCLEOWzr67DmCa/35UTNCXj1XNE5cfn9/u3jZMffkHMd3Sl1Yiu0aEaQQJxSJrPdeOJn9ZFLpFNK4kTNCXGgNIR5djPo5Xule51tnbbSvTQnl8ahsnpqmtoINxnI1ZT7a6cL0evdLcepb3ZwRBkM9s5c/ghIttK9M2hqtXD7W3n8cslWXtzWbP8ZfcRaLW6mS0lgcFrHg7ySNRWAccpB3lx/wKlVq57ddFqEdpDvoZBznTH940mMEkHtU2TIee8mNhNQRICo3nRAL5fryTwpEBeg+kyMPibplJKcTCiKzJWSSCTO2MLOt7l+vS/lSQWByyYPYG5uKi0WK796ZxttFiuMvADOexyyZwpBql0kRm1TKz99bRPPfXvAtmzVvtLgDCgs0nYdP8G6k2mDkrhgnOeJNrPJwI0zhWj0/KqDqHrpXs5M273SZ9uLqG+xkJMcxdRBnq/NzxqVgUGBnSdqOFrRIBbGZwkBqa2RokJRJjjCi857OsPShSiVX1KHqosDGeNgwSPi+fK/wI73vN4fIJwvL59jC4V3ipqQeEdroz3Y319RKmWYcBdatPvg8DiRDx0IwQ47b66Dek1DkJlSEsCeJxU/QNxUuiMhW/xTW1rsHQHAfcj5sSpACCJhRvGnPG1ICoNSoqlrbuODvHYldXoeTWU7F5U3HFgOQHX/2Vz14nd8pM087G/SDs5+lO9VFItxlKqJDEiM6rhC0hDU8HgilFbCyvew/oDdzrhyXykGrGQ3a7+nHhaljAaFhxaN5f9Oy2HeyLTON5CELiaz/cSgn+xtnfeyXG/TnbQPO5dOKcnJhhSlJBKJI7qjp2i769dtnfekKAWijO/hS8YRF2Hih2PVdqHplBvh/z7tIEgdKa9n0TPrWL6nhHCTgZlDRZXE6v0uuoX7ydG4CQBMM+zhzxeORvEiKuHKqQOJizBxsLSeyt0rxUKHPCm9dO/SyQM63V9StJlpg9qV8JnMtnsngxYY7YtTKic5GqNBoa65jbrCfLEwMQem/gROvVV8//4t9niUzijZLQSpqiMQpVWqBDsY+2SgeJeo/olK8X+y2WB0dhIG4pKy7UObXK4NklNKF0IjEyEyITj77EKkKNUdeJMnBULZdxV2bhOlnMWOvIIqAKdQbYNB4UfTRD7T6+uPOLmLyJwoHje+6Fsdc8VBqDyMajBx6edGthZUER8ZRr/4CE6o4gDuFLzs7W6LxKxDnTkFo8HFycJgQNHK8iYa8nltvfg9Hq1oIL+kjlzjcUyWBjDHhkRGwDlj+vGnC0bbBEJJL6Z9rpT+2NPle9Cxk6Z+QRInRSnJSYIUpSQSiSO6iFKyq2OAdGOlfaK3h7o0hyLpcRH85SJxz/HP5fvZpZWntWddfhkX/Wst+0vqSI8L5+2bp/Prs3MBWH+gXLisAqTVYuWZg2Li/YzIfEb2886NFBNu4trp2YTTQmxZnlio5UkdLqvn+0MVKApcMtlz6Z6O6xI+ce82gBKSo82kxoR7tS8Qbq6cZDHpXlfkIEoBzH8Qcs8VTpslV9mzf9xxfAu8cq6IbkgbBTdqTaBaaqG51vO2EmccQ84DyYl1zKMKiigVZKdULyrdAylKdQ/edN7TSXeRK1Wv2WPbi1LHxAX5+CznbmCXTc4iIszAnqJaNh+ptL9w2m0QFiVmjHZ/5P34tdK9TZZh7K+GwSnRfPDz05g7Io0iVbPDNlf7fFBsLBc3+q1RHlpxOohSy3YXU1jdyErNLnxhsnYznjnBswNNIvGVDqJUiJTvgb1MTx+TPqMSSMCiRNKbkKKURCJxJCHHXmlQutf5teObxWPSYBHqLbGxcEJ/5o9Kp9Wicvc722hpcxaYXl9/mGtf3kBVQyvjsxL46BczGZ+VwJj+8SREhVHb3Gar2giE19Yf4aOKLCwYSGwphCrvS9KunzGIqWEHCKONlsh0W8e8/2lZtLOHpdIv3rtGSGePFqLA5iOVlNQ0iYXaDf1ApYQR/WK9cnA5oudKWXXRSRcIDEa45N9CGGkogzcvFwKqKw6vhf9cCI0VojLk+k8heYj4n4fguKVWPAjPniY62waK1QKrH3dfTtvTBNp5T0fPlYLgXIPbmocFyyml/8+FfukeSFGqe/DWKQV2p1SxK6eUXbxpbrPYQvcmZjkHa8dHhXHhePGP/bpj4HlshujUASIYz4t2pKqqcvj7jwH4pm0sM4Yk8/6tpzEoJZqx/eOpJ5J6RWvX6uOHqE07iBrjPXyQNav1dPNBLFaVJd8XsFLLk5odrbWB7eHSPUkfpH3YuS3o3LvZti5FH0PNMVEXr1/ESKeU5GTBJkpV9egwJBJJiGAwOJTwtQs7l3lSblEUhb9ePJbEqDB2Fdbw9DfCzdNqsXLf+9v5w4c7sVhVFk7IZOlPTyU9LgIQkRWnDRHlY6v2BVbCV1bXzOJl+6gnkqr4kWJhwXqvt0+NDef6TCFAbTaMBkXBYlVtotTlU7yPXciIj2DiwAQAvtTdUrooZShhpA95Ujp6B76IuqNO+wPAHA1XLRUTnmX7YOm10NbivIN9X8F/FwlHVM4suO5DiNIMAXpsQzDKvba8Lu499WyuQNj9scjLeu+nge+rKwi0855O0J1S+t9TOqUkXYUvTinbSdVRlHIIOtfYXVhLi8VKUrSZrKSOMwDXnpoDwGfbCymra7a/MOM2iEqG8nzY8prHobRarPz+va0kl34PQNTI+fznhqnEa4HeY/uLG4MTulvKxwB1U70Q2yKTPbhPNMEp03KceOp4c8NR1mnZUoNbtNmwHu68J+mD6NlR1UeFG6OlTnwfEk4pvXzvhD1PyhQJEQk9NiSJpFuRTimJRNIed7lSMk/KI6mx4dx/kajS+Nc3+azaV8o1//6eN74vQFHgtwtG8MQVE4gIc65ImDVMiFJr8gMTpV5ff4Ta5jbG9o8nceRcsfDIWp/2cZpRhH5/VDWIvUW1rMkvo6imiYSoMM4c5VvO6zmj25XwaTf0WUoJI7wsK3RkWHoMRizEtzjvz0ZcP7j6bdFZ9vBq+OQOe8TKjvfgraugrQmGL4Af/c+pC7ttMjJQZ42l1W6AKN8f2L7AHiJeugfK8gPf35H18O0jwoEVKG0tIlMKAndKpQwXFUgQnGZDuijlR06zS/S/g+YeDHWkKNUd2JxSOZ2vmzYSUERavi5G6QeKaPuBNa9AuCPGD4h3aSUdOyCe8VkJtFpUlm50sMFGxMHpvxHPVz4skvldUNXQwo9f3sDeTd8QqzTSFJbAz6+62CkvaXh6LGajgeMWTZTy8aAY3SJOZAnpA92vFJUESUMAOD26gLK6ZhpbLQyMhYgKrfOEdEpJgo2jU0o/OUQmgtlFIH9341i+55gnFUhdvETSm9AFWClKSSQSHVsHPgenlNXq4JSSE5juuGB8JueN7YfFqnLdyxv4/lAFMeEm/n3dFG45fYjL+4yZmiiVd7SKmqbOKy/c8fkOcR3zf6flYMjRQsqPrPN+B61NRBRvAeA76yieX3WAt7WA84UT+hNu8i3eQ8+V+u5gBZX1LaiO5XsZ3oec6wxNi6GfUoEJC6rR7LopTcYYuOw/oBgh7w1Y/Q9hHHj3RrC2wZhL4YrXISzCebvYIJV71RYCmhAWDBHJUdja80lg+7Jaxe/hm7/C3s8D2xeI3Dlrq7iO8MYs4gmD0X4PGgzhJ32UeCzZBfXlntftjKZqOCpMJeSc5nndEEGKUl1NW7PdzeDNP7852v6PrZfw1emZUvbyvW22PKkEt7u69lTxfm9+X4DF6hBsPvn/hEBWXwLfPdNhu0NlosvGugPlzDPvBCAidx6K0eQ8VJOBEf1i7U4pH5TduuY2kqziA5eRmeN5Ze1C4qrMYtuiqwZUoqgWcXAPhjotkTjiKErZ8qRCoHQP7GOrK4YqrYRV5klJTiakU0oikbRHDzsv2m53mlQcEGW+pgh7ZqvEJfdfNJrkaDMAA5OieO/WGcwb6T7zdUBiFINSorFYVafu2L6QX1LHvuI6woyKeK+Bp4oXyvbZ73064/gmsDTTGpnKITWDj/JOsGynuF+4bIrv123ZydGM7BeHxaqybHcxJSYhImUolQxN9D2/dkhqDAMVYTKwxA8UpaauGHYmnPuIeL7iQfjol6BaxT3bohfAGNZxG90pVRtgppSjqBUMp1SZoyj1aWD7OrbRfn8ZjIwqvby337jgTOZe+BRc/AIMOSPwfcUPEMcp1Qr5ywLb14EVQtBMGS6dUhKNqqOAKux90SnebZOhnTiLdogTq4vue9uOVgEwwYModf64fiREhXG8qpFvtBwmQLQ4PeMP4vnafzod+L/cWcSFT6/hYFk9/RMiuT5daxPr5sM2tn88hXoHPh9EqcNFFSQpwqUVm9pJvbdmuZ5kPGDr0jcnRuZJSboQvXyvvkR0n4TQ6LwHooWt0QyocELMDso8KclJhRSlJBJJe1JHiHNjc7U9S0Uv3cuc6PqmXmIjOSac126cyl1nDefDn5/G8PTOXUG2Er79/pXwfaG5pGYMSSE+MkxUR6Rp2boFXrqltAyksCGzmT44hTarSovFyujMOEZnxneysWv0Er4vdxSxq8pErSpiUiLqfS+riggzMj6mCoDaiE6uI0+5Cab/wv79abfD+U+4b+ZkK/cKUJRyjF8pC1CUslpFRIzOsY2BZSTtfN/+vH1enD/oTkrdWRkoSYNg/BXBa7g1/GzxuO+LwPaz70vxOGx+YPvpRqQo1dVUHRaPCdneK7LpWl188U5x0W3RMqE0Uaq6oZWDZfUAjB+Q4HY3EWFGW8CfU+A5wOhFomtASx2seoRWi5UHP9nFza9vprapjcnZiXxww0giSzRVevBcl+8xtn88ReiZUt4frE8cOwxAKyZRFuUJTXgKL9rCfQtGsGhSf4Zb9jq9JpEElchEe524bn8NhZBzELNsujtQv+B2ZQeXSPoqUpSSSCTtMYZpERjYb15teVKydM8bRmfGc9u8YSRqjqnOmDk0sFypz3cIsWLBGIeQ6OwZ4vGwl7lSejB39mncMmeIbfFlk/2/ZtNL+FbvL2PzkSqOqpopQBc7fWRMZAUAxSYvrtXOekB8LXwOzvyL53tH/Vow0KBzR6dUYwU0VPi/r9oT0NoABpOW2aTC3s/825fVCrs+sH/fPi/OH3S3VaB5Ul3F8AXiMX+5Vw3JXGK1wH7NaTX8nOCMqxuQolRX40vnPR3HDnx6rlR4HIQJpV5vv5qTHNXpieNH00Re06r9pRwpr7e/YDDAWX8BQN30Mrc/8x7/XiNaR940cxBv/fRUUku/A1Qx++TGJTJ2QDwnNKeU6oNTqrxIOJ1qw1I7F+vSxwjrdVMVN4y08vjlEzDqDhEpSkm6AkWxi1C6KBUKIec6+lj0E7QsYZWcTEhRSiKRuKJ92LnsvNelTB+SjNGgcKisnqMVDT5tW1DewM4TNRgUOGuUQ5mgLkp5kyvV1mwXHnNmMXtYCrOHpzIwKYqFE/2/ZhueHsPglGhaLFZeW3+YIwGKUoOMoiLlsCW185UNBtQZv0Qdf2Xn90fBckq1v38LxC2lb5s4CEYtFM/9LeE7+r0oTTTHiO9rjgeWtWS12KNxguWUCjb9J4mKiOYa37LVHDm+BRrKIDzeXhLbC5CiVFfjS+c9Hb18r3SvaPsOTqV7eVrpnqc8KZ3s5GhmD09FVUW2lBOD51DRbxaKtY0FJS8SG27iuWsm8/vzR4lA8/zlYr0h89zuf3h6LGUGcZBVfXBK1ZeJEMKWKC+6YpjMwtUF4uRTV6pl6SjCki2RdAW6KKWfrEPFKQV2UcraJh6lU0pyMiGDziUSiSsyNPdD4Q/QUi8qDkCKUl1EbEQYE7V7EV/dUl/sFELKqYOTSY4Jt7+QrYUyF++AxkrPOzm+WXSmi06DlGEoisJrN0xl1T1zSYjyzu3lCkVROFtzS9U0tVEQoCiVYRWOsF2NSZ2uu+tEDWc9sYofv7KR5rZOus3p14L1JWBp82tsQEdRKpBcKb10L2UYjDhfPD/4LTTV+L4v3SU14nx7LlIgJXzlB4SLyxQpxheKGIz2kju9BM9X9mvbDT2jV5UtS1Gqq/HHKRWfJdRNa6vdluoYcq6LUh5K9xzRA8+XbjpKU6s4wFmsKo8v28c1R87FqipcYPyOr66IsVlWUVU48I147iG8LcxoID5d7N/QUuv1Qae1SpyMFG9vpnXr9fFN4iQEkJoruglKJF1BexEqlJxS7Z2L0iklOZmQTimJROIKW9j5D3AiD1SLOHfLc2SXoXfhW73fy2ByDZelewCx6ZA8FFCh4HvPO9HvkXJOC3oHYj1XCrCX71Uc8mtfcY1C9NlY4/me5dt9pVz23DryS+pYta+UR77Y63nH0amiTE612vOH/cHW0Ee7tgyGUyp5KKQOh+Rh4n52/1e+7cdqhZ0fiOejL3ZwQQYgSunbpo8OXgZUV5Crldz5myulbzfs7OCMp5uQolRX449TSlHsJXy6W0lzSqmqanNKTRiY4NXuzhiRRv+ESKoaWvn0h0LK6pq57uXveXL5fnZZs8lLEopsvw0P2TuWlO0XLi1juN1K64bhWelUqdHiGy9K+KxWFWO9OBlFJHp5oaCLUsc22kUpWbon6Uri2wXwh0rQOXQUyKRTSnIy4ShK6ecsiUQiSR8NKKLkR8+xkXlSXcqsYaJaYm1+uXOnbw8UVjeytaAKRYGzR2d0XMFWwtdJrpRDnlSwGTcgnsz4CADKw7R7FX+cUk3VmJqF4+uHugSqG1znBC3dWMANr26kvsXCiAwRMv/SmkPOjaraYzBAjPb7C6QDn54pNXiOeHQMKvcV3WWlO5FGam4pX0v4jn4HdUXCpDFkrr3cLpBcKVueVIiW7ukMnguGMNE91FeBsOaE9jtSYNhZXTK8rkKKUl2NP04psJfw6R+gaCFKHatspLy+hTCjwqh+3rmEjAaFq7VsqX99k8+5/1zN2vxyIsOMPHHFeCZd96joWHJ4tV0EO7BCPGZPB3OUx/2LDnyaJdULUaqwpolkVYToxaR6WRLVX7uoKN5pPwn1n+TdthKJPzg5pRSIDaGZVidRSoFYFxd1EklfRRelrK3Q2tizY5FIJKFDeKy9zCfvDfEoS/e6lPED4omNMFHd2MqO4965V7/QXFKTByaSFhfRcQVdZPKUqdPWAkc3iOc5M30Zslc4lvCZU7X/qcrDvk+EaPeBlcRRTyT5pbVOL6uqyj++3Mtv3t2OxaqyaGJ/PvrFuZhIxAAAWT1JREFUTK6fkQPAr97ZRnFNk/v9692Xa/wMO29rsbusBp0uHoPilNJEKb2Eb/8ykQHmLXrXvRHngSk8OKKU7pQK1ZBznYg44f4D391SesnfgFMgOiW44+pipCjVlTTXii4G4JtTCkS4NwDawU9zSukh5yP7xRER5r318PIpWYQZFQ6W1VNS28zQtBg++sVpXDxxgBDMpv5UrPj1n0QQ3AE9T8p96Z7O2P4JFGph59aqzkWpg6V1pCFmDYzeWqrjB4jZAGubvU1sfzn7JelCHEWpmDSRbRYqOLq2olN7Vc24RBIw5mhQtPOfLOGTSCSO6C4IPY9IilJdisloYMYQcQ/gbQmfXrp3TvvSPR3dKVWYB811rtc5sQXaGiEqWTRk6gJunDmI04Yms3DONEAR71fnwbnkCs1dVW4W9zv7i+0/T3ObhTuW5vH0N8KZdNsZQ3ns8vGYTQbuPXcEo/rFUVHfwp1L89y70HSnvL9OqboiQBXmBD0Uu+KgfxlVLQ1QLTKDSRkuHjMnifu3llo4tNq7/VgtsOsj8Xz0QvGof67L9on38RVVFVlzELoh547oXfh8zZXSyySHzw/ueLqBHhelnnnmGQYNGkRERASTJ09m9Wrv/mHXrl2LyWRiwoQJXTvAQNBdUpGJvmcf2UQpDS1TKq+gCvA+T0onNTacy6eIcqSFEzL58OenMSw91r7CrF8Ji2TxDtj6X7sbyQtRalh6DMWKUGOrizuvtz5YWk+aIn4Orx0eiuJswTaG20scJZKuwFGUCqU8KYA4x7HJ0j3JSYaiyFwpiUTiGscbTltbeklXMlMr4Vu9v/Ow89LaZjYeFhP2C8a6uX5JGCgiFKxt9u567XEs3QtynpTOgMQo3rjpVOaOzrJfE/pawqet3xgt7sH2lwhRqrqhlete2sCHeScwGRQeuWQcd83PRdF+lnCTkaeunkiU2ci6A+U89+0B1/vXJ/f9dUrZ8qQyxe/cFClcyHr8jC9UaGOMTIRoIVRiMAi3E8Cej73bT4FD6d7guWJZTLqYhFWtULLb97HVHBdGEcUIaaN837670UWlI+ugscq7bVqb4OBKbftzumJUXUqPilJLly7ljjvu4L777mPr1q3MmjWLBQsWUFBQ4HG76upqrrvuOubNc98VLiTwJ09KJ20kKA5/Hk2U0p1SE7zovNee+y8aw+p75vLEFROIDjc5vxiVBDPvEM8/v0d0J4hO6yiOuSDMaMASI04sNSWdH8QOltaRrmgzWL5k4TiKUv3GS3eIpGtxFKJCKU8KxOfVpFneQ6msUCLpLqQoJZFIXOGYF5MxFsIie24sJwmzhoqJ6S0FldQ3e3bYfLmzCFUVZX/9Ezz8bWy5Um5K+Gwh57N8Ha5/JOaIRz9FKSVJbL+/pI6jFQ0senYt3x+qICbcxCv/dwqXn5LVYdMhqTH85UIxAf/4sn1sPlLRcf+BOqVqHELODQZIHiK+9ydXqn3pno5NlPpMBJh3hl66N/J8e5WCojiEnW/zfWy6Syp1BIS5KBkNNZIGQ0quaNaQ/7V32xxeI+7f4/p7df8eavSoKPX4449z4403ctNNNzFy5EgWL15MVlYWzz77rMftbr75Zq6++mqmT5/eTSP1E3/zpEDkOCUNsX8fk0qrxcp2rV57vB+ilNGgkJUUZVPhO3Dqz8QNbptWuzzkDK9nHyJTRGaVpfJYp+seLakgQakX3/iSheNYrieDKyVdjSncluXWIfS8p1EU++yYdEpJTkakKCWRSFzh6JSSpXvdQnZyFFlJkbRaVL4/VO5x3S9spXudXLt4Cju3tMJRrTNfTvBDzl2i38v5KUpFZwwF4IdjVVz8zFoOlNbTLz6C//1sui0s3hWXTh7ARRMysVhVbluSR3Vju6D0QJ1SNe067yWLcfqVK6ULWSntRKmcWcL1VF8iuqh7wmqBXR+K56Mvdn4tkFwpW55ULyjd07F14fOyhM/WdW9+l7kHu5IeE6VaWlrYvHkz8+c71zzOnz+fdevcB9u98sorHDhwgD/96U9evU9zczM1NTVOX91GIE4pcC5Pi0lnX3EtTa1WYiNMDE6JDnx87QmLhLm/s3/vRemeTnI/EQJobijqdN3aUiFcWY3h9hsLb8icaHePyc57ku5At2uHWvke2McknVKSkxEpSrFq1SouuOACMjMzURSFDz74oNNtvv32WyZPnkxERASDBw/mueee6/qBSiTdSUyavSOZFKW6BUVRmDm08xK+yvoW1h8UotUCd3lSOnrY+bFNoizJkRNbhSMkMglSR/o9bp9IHCQe/RSlUrNyAahqaKWsroWR/eJ4/9bTGJHhOd5FURQeXDiG7OQojlc1cu97P6A6hq0H7JTSxCxd3NIFpXI/RKmyfeJRF7Z0TGZ7OdruTkr4CtYL8Soi3h68rqM7pXTXky/0pjwpHb0EL39Z5xlfqmoXr4af3bXj6iJ6TJQqKyvDYrGQnp7utDw9PZ2iItfCxv79+/ntb3/LG2+8gclkcrlOex566CHi4+NtX1lZ3eh4CMQpBfYOfADRqeQdrQJEnpTB0EUK6ISrIWuaqNv1oZVkVo44ACW2lmC1uLdmNrS0oWoHTjWmn29KbniMsIBGpXQ8UEkkXcGQuSKTIjsEXZmD54ja+FAcm0TS1dhEqaoeHUZPUl9fz/jx43n66ae9Wv/QoUOce+65zJo1i61bt/K73/2O2267jXfffbeLRyqRdDNzfye6fuWe29MjOWmYPUyU8HkSpZbtLsZiVRmREUtOZ5PryUOFW93SLELNHbHlSc0QJWfdgT/le1YLVIlImpiMofSLF2Vjpw9P5Z1bppMR710ZWWxEGE9eORGTQeGz7UUs2XDU/qLNKVXoe2dAgGqtwkWfhNVL78oCKN/TQ84dsZXwfeJ5nLauexd0bDCk58MV7xS/W1/ojU6pAVMhIkE0bTi2wfO6pXugukBEe/TSe2TvlJ0upH0pmaqqLsvLLBYLV199NX/5y18YPtzFP7sb7r33Xu666y7b9zU1Nd0nTNmcUjn+bZ+uKcKRSWAMY5suSmX54C7yFYMRrv8UUMDo/b9H9iAhSkUrTRwuLCRngGtnyaGyetK1kHNjvB9lR5e9BqhinBJJVzPvjzDrblFOG2rMvhtOvTU0xyaRdDXSKcWCBQtYsGCB1+s/99xzDBw4kMWLFwMwcuRINm3axD/+8Q8uueSSLhqlRNIDTP6x+JJ0GzOGpGBQIL+kjsLqRvrFd8yL0kv3znUXcO6IogjRadcHooRPL+eD7s+TAv+cUjXHRWi40QxxmSy+Ioo9RbVcPW0gYUbfxLTxWQn8+uxcHvp8D3/5eCdTchIZnh5rd0q11kNzjW8VKODCKaW5nHx1Sqmq+/I9gKFniiZVFQehdC+kueiY6Kl0D0TOUliUcMmV50Nqrndja6iwdwXU3Va9AaNJlOJtf1uU5jl+Btqjl+7lzOq19wU95pRKSUnBaDR2cEWVlJR0cE8B1NbWsmnTJn7xi19gMpkwmUzcf//9bNu2DZPJxIoVK1y+T3h4OHFxcU5f3YKqBu6UypkpWmlOug6AbUfFxfeErMRgjNA9xjCfBCkAU0QMNYr43R486P5AJjrv6SHnPuRJ6RgMUpCSdC+hfHAP5bFJJF2JFKV8Zv369R0iE84++2w2bdpEa2ury216NAJBIpH0GuKjwhindQZf48ItVdPUyur9pYAXpXs6egmfY9h5T+RJgd0pVXsCWhu920YXsBIGgsHItMHJ/HhGjs+ClM5PZg1m9vBUmtus/PLNrTS1WsR1oH4+rPGjhK9DppQmKNUVQ5MPx/vaImipEw5+XcBzJDxWOPzBfRe+I2uhvlS4gwa7cPsYjPYAb19ypQq1YPTEHN9Fu55GL8XrLFdq31fO6/dCekyUMpvNTJ48mWXLljktX7ZsGTNmdFQC4+Li2L59O3l5ebavW265hdzcXPLy8pg2bVp3Dd07GsqFag3+hySHx8BPv4Gz/kJdcxv7SmqBLnZKBUB9hAiFLjnqpm0pQpTSnVI+dd6TSCQSiUQnIkE8SlHKa4qKilxGJrS1tVFW5rrkpkcjECQSSa9ilocSvhW7S2i1qAxJjWZYeqx3O9SdIQXf2zN1CrcJ8SMiAdJGu9006EQlgVkbd5XnLvE2dFFKF7QCxGBQeOyy8aTEhLO3uJYHP90lXtCzRWt9DDtva4G6EvFcF6Ui4mwd331yS+nrJmZ3LLvTsZXwfer6dVvXvQvcd1i35Ur50IGvqBfmSekMnSeEvtI9UHHI9ToNFXD0O/FcilL+cdddd/Hvf/+bl19+md27d3PnnXdSUFDALbfcAojSu+uuEy4hg8HAmDFjnL7S0tKIiIhgzJgxREd3QfB3IOguqdh+QWk9uf1YNaoK/RMiSYsNzVaWaqw4oNWUHHa7zsGyusCcUhKJRCKRyEwpv3AVmeBquc69995LdXW17evo0aMu15NIJJKZQ4UotTa/DKvVOTfo8x3CxbOgs657jqSNEsf61noo0kQIW57Uad2XJwWinNDXXKkgi1IAqbHhPH65yFb673cFfLmzyN6F2VenVG0hoIrywugU+3J/cqX0PKlkF6V7OrkLAEUE1etZVjqWNnsI+uiF7vfRz48OfIW9ME9KJzLRLs66c0sdWAGqVXxeEgZ239iCTI+KUldccQWLFy/m/vvvZ8KECaxatYrPPvuM7GxR7lZYWEhBgZdqdKhRdVg8+tt5rx153ZEnFSCRKWIG1VJ1vMPJSOdgaT3p6KKUdEpJJBKJxA9k+Z7PZGRkuIxMMJlMJCcnu9ymxyIQJBJJr2PiwESizUbK61vYVWgv/apvbmPlXlG6d463pXsgRKeB2g25XsJny5OaGYwh+4Yex9KDohTA7OGp3DxbdD1/duUB/51SjnlSjhMT/uRK2ULOPYhSMWkw8FTxfO/nzq/ppXuRiZ6DunWnVNF274PdbSHnE7xbP9SwlfB94fp1ffmw+a5f7yX0qCgFcOutt3L48GGam5vZvHkzs2fPtr326quvsnLlSrfb/vnPfyYvL6/rB+kPgeZJtUMPOZ+QlRCU/XUFcek5AKRYSjlUXt/hdVVVOVhaR7rulIrpmB0mkUgkEkmnSFHKZ6ZPn94hMuGrr75iypQphIW5KZWQSCQSLzGbDJw6WAjca/LtJXwr95bS3GZlYFIUozN9FLZ1l8jhtcJNU6CVKXVnnpROCDildK6bIfa5/Xg1zVHa/ZSvTilbntQA5+W626ncB6dUuReiFNhL+Ha3y5XypnQPhBtIMUJDmeb06oSWertg1hvL9wCGnyMeD6+B5lrn1yxtkP+183q9lB4Xpfosts57QXZKaSGCoYgxQTil+inl7Dje8UahuKaZ+haLXZSSTimJRCKR+IMUpairq7NlbAIcOnSIvLw8m8PcMQIB4JZbbuHIkSPcdddd7N69m5dffpmXXnqJu+++uyeGL5FI+iB6rpRj2Lm9dC/DbamwW3TxqWCdlidVK47/euB1dxJColT/hEiyk6OwWFUOtWhCnzcijSM2USrTeXlKF5XvgV2UOrwGGrX7QUsb7P5IPHfVdc+RsEhIGS6ee1PCV7wTUIURIraXmiFShkHSENHJ8UC7xm7HNorfY0QCDDilR4YXLKQo1VUE0SlVVN1EUU0TBgXGDgjd8j39oNZPqWD7sY43CgdL64ikiVhF61ohM6UkEolE4g9SlGLTpk1MnDiRiRMnAiKnc+LEifzxj38EOkYgDBo0iM8++4yVK1cyYcIEHnjgAZ588kkuueSSHhm/RCLpe8wclgrAhsMVNLVaaGq18M0eEabtU+meTsZ4CIsWx/oNL4hlA2f0TCduvaucN6JUU41oegVBMyi0Z8YQ4UrbVhUpFtT4WL5XrYlS8f2dlyfr5Xv5YLV2vp/WJnv4e2dOqaTBIqBetdg7xh1ZI35XkUmQM9vz9uAQdv5D5+vqgei91SWlo7ug2udK7de+H3YWGE3dO6Yg07tHH8oE0Smlu6SGp8cSZQ7hP5nWuaGfUsEPx6o6vHygrJ40vfNeWLRoDyqRSCQSia84ilKq6pyHcZIwZ84cW1C5K1599dUOy04//XS2bNnShaOSSCQnM0NSo+kXH0FhdRMbDlXQ3GalvsVCv/gI/6o9jCYYOE04RLa/I5b1RJ4UQJKDKNXZeUe/D4xKFh3tuoDpQ1JYsuEoa4rNXAG+i1I2p1Q7USohGwxh0NYo1knopOtqxUFAhfB4iE7t/H1HnAclO2HPxzD+inale17c5/YbB9vftmdFeaKoF4ecOzL8bPjuX0KUslrtIf+6SDWs93bd05FOqa7AaoEqrUNNEJxS2zSBJ5TzpADbQS1KaeZYYWGHsPODpXUOIecZJ+VNhEQikUiCgC5KWdugtaFnxyKRSCQSQHTy1Ev4Vu8vtZXunTMmA4PBz+t+PVdKtYjHnsiTAojPAhRxzqkv9bxuF5bu6UzX8rvWl4aLBfWlYGn1fgcOotRH207wq7e3seFQhRCGdAHOm7Dzsn3iMWWod/d2eglf/nKRkbTLy9I9HVvYuTdOKW2d3u6Uyp4B4XEiS+v4ZrGsqgBKdoFigKHzenZ8QUCKUl1BbaGo+zSYOqrPftAbQs4BCItAjRIHyLiWkg5h5wdL62WelEQikUgCxxwtwk7hpC7hk0gkklBDL+H7Zm8py3YVA7BgTADX/dkOIlR4XM8JDCYzxGuh4J2V8HWDKJUaG86wtBjKicWqhAEq1BZ1up0NzVnVGtOP+97fzrtbjnH58+u54vn1lEcMFOt4kytlCzkf7t379hsvBL7WBlj+ADRWCEdZzizvttf//pWHPZ//La1CtIHe75QyhtmFJ73bnu6SypoGUUk9M64gIkWprkDPk4ofEHDNs8Wq8oOWzzQ+1EUpQLGV8JV3yJU6UFpHmk2U6qVhcxKJRCLpeRRF5kpJJBJJCDJzaAqKAvklddQ2tZESE87k7ET/d5g5CYyaG2jg9J7Jk9LxNuy8G0QpELlSKgZqTJoo4W3YeVsL1Imsr61VUdQ2tRERZiDMqPD9oQreOSxyqo7l/+CxTBywC1d6FlVnKIrdLaXnhHlbugdCgNE7BhbtcL9e6V6wtAghMyHHu32HMu1zpfZrmVzDe3/pHkhRqmsIYp7UwdI66prbiDIbGZ7eCzKYtBmEfkoF2x068DW1Wjhe1Ui6niklnVISiUQiCQQpSkkkEknIkRRtZnSmPUfp7NHpGP0t3QMIi7B3FuupPCkdPZal4pDn9bpJlJo+RJRKHrdqop+3uVK1hYAKxnA+PyhK/i4cn8mqe+Zy/YwcChTRvOrgnjwueHoNX+wo6hDLYsPmlOok5NwRXZRC26e3pXs6uvPJUwc+vbwvY6w9g6k3M/QsUapXvF10Ozy0SizvA3lSIEWpriGInfe2aqV7Y/rHB3ZA7y5sHficnVKHy+tRVRhgqhILZOc9iUQikQRCZIJ4lKKURCKRhBQzh9oDrwMq3dM55yGY8Us45cbA9xUIIeaUOnVwEooCh1q0SRpvnVJanpQal8nXWnfEM0em0y8+kj9fOJpf/+h8AIYaCtlxvIZb/ruZBf9czcfbTmBxFKdU1cEp5YMoNXAGRGpCWlQKZPsoNtpypTyIUn0lT0onOhkGTBXPv/wdtDVB/EBIG9mz4woSIdzKrRcTRKeUnic1sReU7gG2DK1MpZydJ6qxWlUMBoWDpSJfKiusFlqRTimJRCKRBIZ0SkkkEklIcvrwVJ779gBJ0WamDQ5C3k2/caGRC5To0IHPHVaLCKGGLhelEqKEK624WPsde+uU0tZrjEjnaGEj4SYDM7WAeoDErNEAZCpl3D67Py99X8ze4lp+uWQrv1yyFUUBg6KQQhXfm6uxqgoTnt5Pm1KAApiMBn42Zwi3nD7E9fsbTZB7HuT9F0Zd6H3pno5NlNrmfp2+0nnPkeFnw9HvHEr35veZxmHSKdUV2JxSOQHvKk8TpXpDnhRgF6UMldS3WDhYJsSog6V1AA5B59IpJZFIJJIAkKKURCKRhCSnDk7i4UVjeeHayYQZ+9DtpjdOqdpCkWUUpIZXnTF9cDJFaqL9vb1Bc0odtQgxa+bQFKLMDsJQdLLNyXTnJCNrf3MGd545nPjIMEAYpCxWlRxViFvH1BRqWk00tFiob7FQ3djKU8v3U9/c5n4MZ/4Z5t4H8/7o/Q+ro7ufSvaIfKz2WK12p1S/8b7vP1TJXeD8vZ4z1QeQTqmuIEhOqaZWC3uKaoFe0HlPJ14cfLNNldAM249XMTQtxuaUSrCUi/WkU0oikUgkgWATpap6dBgSiUQicUZRFK6cOrCnhxF8dFGq9gS0Nom8q/boglXCwG4JZZ8xJIX31+pOKS9FqWohSm2vjQbgzFEuGlAlD4NjG6BsP/EZY7n9zGH8bM4QqhtbUVFRVYjYdhyWQ9rgsay+cC4AVlXl+lc2cqisno+3nXD/fxCTCqff49PPaiNhoLgGaKqG0j0d3VCVh6ClVgTke9sVsDeQOkL87FUFEBblfcfCXkAfkq5DhLZmu3UywEypZbuKsVhV0mLD6Rfv4qAXimgzAqnWUkBl+7EaAA6U1RNNI2ZLg1gvRnbfk0gkEkkASKeURCKRSLqTqGQwa42n9BK99nRTnpTOKYOSKFGEKNVaddy7jTSnVF5NDADzRqR1XEcPLi/Pty0ymwykxoaTFhtBelwE8Q3CiBGRMYKspCiykqLITo7milOyAFiy8ag/P1LnKIrdLeUqV0ov3UsfBcawrhlDT6AoqFqwuTVntmtRtJciRalgU30MUIV6GZ3a6eruaLNYeeLrfQBcPW0gSm+pF9WCzsPUFhKoY/vxKlRV5WBJnb10zxwL4TE9OEiJRCKR9HqkKCWRSCSS7kRROi/h62ZRKibcRGK6MEIotYWitq4zNFGqSE1i/IB40uJciBvJQ8Vj2X73+9Ff09fVuGTSAEwGhW1Hq9hdWNP5eDzQ0maluqG14wu2XKkfOr7W10LOHfi3cilvtp3B08Zre3ooQUWKUsHG0bIZgJD0/tbjHCytJzEqjBtnDgrO2LoDU7hNjBNh5zWU1DZT29wm86QkEolEEjwiEsSjFKUkEolE0l3olTAhIkoB5A4VriaTtcm7knatqqdQTeLMkW6qV2xOKQ+ilP5ainPnvdTYcOaPFvt9a4MbR5mX3PrGFk7529fsK651fsEbp1SIhJzvLap16krvL8erGvnHukp+13YTT283UlLbFITRhQZSlAo2QciTam6zsPhr8SH/2ZwhxEb0MtuhVsKXHVZFQ4uFZbuKARgRo5XuSVFKIpFIJIEinVISiUQi6W5CzCkFcMrw/lSqogpF7awDX1sLal0JAIVqsus8KRCZUgBl+a7dV23N9p81eViHl688RWRJvb/1OE2tlk5/BldsPFzB17uLaWmz8mFeu9JEm1Nquwg2d8TmlOr5kPOVe0s478nVXPivNWwpqAxoX498sYfmNvGztlisvLr2cBBGGBpIUSrY2Drv+S9KLd14lONVjaTFhnPd9JzgjKs70USpSQki3PyjPHFwzI0S38uQc4lEIpEEjBSlJBKJRNLdhKAoNTk7kWJErlTRsUOeV64tREGlWQ0jMj6NERmxrtdLGgSKQQSG1xV3fL3iEKhWMMe4NBzMHJrCgMRIapra+Gy7lwHs7Xhyud2ltXx3ifOLqblgNENzjd0UAlBbBPUlYuzpo/1632Cx7WgVt76xhTarCIa/993ttLRZO9/QBVsKKvkw7wSKArfNEyLgf787Qp2nDoe9CClKBZsAnVKNLRaeWiEC5X45bxgRYV3ftSHoaB34RkQJm+WGwxUAZJu1GwfplJJIJBJJoEhRSiKRSCTdTaIWq+JKlGqug/pSbb2c7hoREWFGmiJEWPnhQ/meV9bypArVJM4aneE+t9gUbr+fdZUr5Vi652IfBoPCFVNE4PlbG3wPPM87WsXq/WUYDQoGBfYU1XKsssG+gjEM0kaK544lfLpLKnkYmKN8fl+rVaWgvIGtBZW0WvwTkAAOltbxf69upKHFwowhySRFm9lbXMvz3x7weV+qqnL/x7sAuHTSAO6YN4zBKdHUNLWxtKvC5LsZKUoFmwCdUv9Zf5jS2mYGJEbaPsi9Dr18z1TltLifQRelpFNKIpFIJAEiRSmJRCKRdDeOTqn2ZW26OSEy0X6O6ibCEsT9V9mJwx7Xs1ZrIed4yJPS8ZQrZQs571i6p3PZlCwMijAo5JfUeX6vdjyluaQuntifKdnCBdbBLeUq7Lxom3j0Ik+quqGV7w+W8591h7n3ve1c/Mxaxv75S2Y/+g0XP7OOS59bT2F1o0/jBiipaeK6lzdQUd/C2P7xvHDdFP50wSjxc63I9/l38dG2E+QdrSLKbOTXZ+diMCjcNGswAC+vORSQeBYqSFEq2Fz7Pty8GnJm+bxpTVMrz2nq6R1nDsds6qV/Hk2USrGWOi1OtJaLJ7GdHAAlEolEIukMR1HKm25DEolEIpEESkIWoEBrvd0VpdMDpXs6iRnCENFceQyr1f05sbBAOKlKlRSmDkryvFPHXKn2lGvLUtyLUhnxEZwxQji4lm70PvB8x/Fqlu8pwaDAz+cOZd5IsY+vd7crI9Qzo1w5pVx03muzWHl6xX6uf2UD0x9azvj7v+KKF77jTx/tZMmGArYWVFHfYsFsNBAZZmTb0SrOf3IN6w+Uez32mqZWfvzKRo5VNpKdHMUr/3cKMeEmLhyfyZzcVFosVn733naPfyNHGlss/P3zPQDcOmeIrVPiokn9SYkxc7yqkU9/8K88MpTopapHCBOZIJTZyASfN/336kNUNbQyNC2Giyf2D/rQug2tfC+ysYhos738MLpZO3BLp5REIpFIAkUXpaxt0NrgeV2JRCKRSIKBKdw2Ad+hhK8HRam0/qKsMKGtjH0ltW7XKzp2EIDwpKzODRApQ8WjR6fUUI+70APP391ynOY27wLPn9aibC4Yn8mglGjmaY6u7w9WOGco6U6pQkenlPvOe59uL+QfX+1j5d5SCqtF57r+CZGcOTKNn88dwpNXTWTZnbPZef/ZfHnHbEb1i6O8voVrXvqef68+iNrJBFhzm4WfvraJ3YU1pMSYee2GqaTEhAOgKAoPLhxDlNnIhsMVLPFSpHtx9UFOVDfRPyHS5o4CUbJ5/YwcAJ5f1fnYQh0pSoUIFfUtvLRaHCTuOms4RoOb+t7eQFwmAErNCUb3iwMg2mzA2KBZLmWmlEQikUgCJSwKDCbxXJbwSSQSiaS7SHKTK9WDopRJK9/LUCpZl+/e2dNUJsSQ9Kwhne/U5pRqJ0qpKpTtE889OKUA5uSmkh4XTkV9i60juyf2FtXyxc4iFAV+MVcIXkNSo8lJjqLFYmX1Pgd3WsYY8Vh7AurLxLWA/jdw4ZR6d4soXVw4IZP/3TKdH/48n7W/PYN///gUfn32CC4cn8mw9FjCjAYGJkfx7s9msGhifyxWlQc/3c0vl2yl3k2wuNWqctfSbXx3sIJos5FX/28q2cnRTusMSIzi7vm5ADz82R6Ka5o8/i6Ka5p4dqWoovrtghEdsqavOTWbKLOR3YU1rMkv87ivUEeKUiHCsyvzqW+xMDozjnNG93LRJjYTUMDSzNR0odqOSTag6DPZMb3855NIJBJJz6MoMldKIpFIJN2Pnh0cQqKUXomSrlSwzk252dGKBmJahElgyJDhne9TF5yqjkBbs315Qzk0VQEKJHkWt0xGA5f7EHj+1AohgC0Yk8GwdNEZUFEUm1vqa8dcqfBYSNLcQ0U/2Mv44gdClHNpYklNE2v2C0HrjjOHMyUnibiIMI9jiTQbeezy8fzlwtGYDAqf/FDIxc+s5VBZvdN6qqpy/ye7+HR7IWFGheevncKY/q4zxX48I4fxWQnUNrfxxw93eHz/R77YS2OrhcnZiZw/rmOlUUKU2fa7fWHVQY/7CnWkKBUCFNc08dp6EYx3txZe1qsxmSFG1P7OH9CGosD8gVoAW0S8X50QJBKJRCLpgBSlJBKJRNLdOIadO9KTopRWqZKq1LD5UDEWF5lFX+8uJlMRglVMmhdNuWLSwRwLqhUqDtmX686p+Cyv7usun5KFosCa/DIKyt2X2+eX1PHpdpGP9Iu5zg4sPVfqm70lzj+b7ogq2g6F7kPOP9p2AqsKkwYmkJMS3eF1dyiKwo9n5PDWT08lNTacfcV1XPjUGr52cH09s/IAr647DMBjl09g5rAUt/szGhQeXjQWk0Hhy53FfLHDdR7U9mPVvLvlGAB/OH+U2y6JN84chNGgsHp/GTuO995rISlKhQBPrdhPc5uVKdmJzBme2tPDCQ7agXFcXD2b7juT68eKUDaZJyWRSCSSoCFFKYlEIpF0N4kuyvesVocu7DndPSKISkY1msXTplJ2nuh4Xvxm1zFSFW15nBf5xYriOldKf57iOU9KJyspiplDhVCzdJP7LKVnvslHVeHMkemMyoxzeu2UnCRiI0xU1LeQd7TS/oJjrpSHkHO9dG/RpAFejbk9U3KS+PSXM5mSnUhtcxs3vbaJx7/ay9KNBTz65V5AiEcXjs/sdF8j+8Vxy+nCYfbHD3dS3djq9LpwXu0ERPfBCVkJbveVlRTFeWPF/fWLq3uvW0qKUj1MQXmDzcr467Nz3aqgvQ79QFdznOSYcIz1mpocIzvvSSQSiSRI6KJUY1WPDkMikUgkJxGunFJ1RWBpBsUIcf4JHwGhKChabm+6UtmhhK+6sZWCQyKfSDWGQ1Syd/t1lStlCzn3nCflyFVTReD5O5uO0Waxdnj9SHk9H247AcBt8zqKXWFGA3Ny9S58DiV8/Rw68LkJOd9TVMPuwhrMRoPLMjhvSYuL4M2fnGoLGH9yRT6/eVeUDN58+mBunDnI63394oyhDE6JpqS2mYe17no6n+8oYuPhSiLCDNxzTm6n+/rpbFHC+MkPhRyr7J2NX0w9PYCTncXL99FmVZk1LIVpg708OPQG4rWDcY1QpanVrInSKSWR9FosFgutra2dryiRdBexORCTBU1N4stPwsLCMBqNna8okUgkEokuStWcgNYmCIuwC1QJWWDsoVvs2EyoKqCfliulu3EAvt1XSqoqhColLlO4oLxBz5Uqz7cv00WpTkLOHTlzZDrJ0WZKaptZsaeE+e0ylJ/55gAWq8qc3FTGDUhws480Pt52guW7i/nNOSPEQt0pVb4f0H6mdk6p9zWX1NwRqSREmb0esyvMJgN/vnA047Piufe97TS1Wlk0qT+/1cfjJRFhRv62aCxXvvAdSzYUsHBCJtMGJ9PUauFvn+0G4ObZQ+gXH9npvsb0j+e0ocmszS/n5TWH+eMFo/z62XoSKUr1IPuLa3l/q/iQ6En8fQatfI9qXZTSnFKy855E0utQVZWioiKqqqp6eigSiTMDL4eMc8EcD4cOdb6+BxISEsjIyOg7jmWJRCKRdA1RyWCOgZY6qD4qxJmezJPSiROT/xlKBSsOVdDSZsVsEoVRX+8qpp+WJ2UzD3hDsuZaKnNRvpfsXfkeCDHn0skDeH7VQZZsKHASpY5VNtjyk355hnuha87wNIwGhX3FdRytaCArKUrcW0anQb3mnopKtt+HAharygd54n704onBc7BdPHEAY/snsP14FeePy/Tr2uHUwclcNTWLJRuOcu972/ns9lm8vPYQxyobyYiL4ObTB3u9r5tnD2FtfjlvbSzg9nnDiI/yHOIeakhRqgd5fNk+VBXOHp3OeA+1or0Sh/I9QDqlJJJejC5IpaWlERUVJW/aJaFDbRQ0lkNkMsT6Vx6uqioNDQ2UlIgL2n795HlKIpFIJB5QFCE+Fe8QAeChIkrFCjEmx1xNY6OFbceqOCUniVaLlW/2lnC1UiHWi+s898iGzSmlCVGWVvvPmuJFBz8Hrjgli+dXHeTbfaWcqGokM0G4gJ779gBtVpXThiYzOTvR7fbxUWFMyU7k+0MVfL27mP87TSuXyxgLB5Zrz8c5ucDWHyinuKaZ+Mgw5o4Ibnbz0LQYhqbFBLSP3y4Yyde7SzhYVs/9n+ziozxRwnjPOblEmb2XamYNS2Fkvzh2F9bw3++P8PO53guGoYAUpXqI7ceq+XxHEYoCv+prLilwUb5XJB6lU0oi6VVYLBabIJWc3IdKjCV9g9ZwaFUgTIGICL93ExkpLoxLSkpIS0uTpXwSiUQi8YwuSukCTSiIUppTamR0PTQKQeaUnCQ2HqqgtqmNwZFVoOJdyLlOklYC2FgJDRXQUA7WNgiL9k3cAganxjBtUBLfH6rg7U1HuePM4RRVN/H2xs5dUjpnjkzn+0MVLN9d4lqUapcn9Z7mwDp/XD/CTaF3bo+PDOP+C0fzsze28Ob3IgR+/IB4Fk7w4W+E6BL409mDuHPpNl5Ze5gbZw4iIiz0fl53yKDzHuKxZSKlf+GE/gxPj+3h0XQB+kGq5oToRiGdUhJJr0TPkIqK6rzlr0TS7Ri0Cy5rW8C70v/HZW6aRCKRSDqlfdh5KIhS2n1WVlgVAOsOlAGwbLeIURkdUyfW80VMMkfZg9vL9juEnA/xPpfKgaunicDztzcexWJVee7bA7RYrEzNSeJUL/KV540UYeffHyqntkk7XzsKUQ55Ug0tbXyxUxgj/O261x2cMyaD+aPsbu8/nD8Kg8H33+354zLJjI+grK6ZD7SIoN6CFKV6gNLaZlbuLQXg9nneB8T1KmL7AQpYWqChTDqlJJJejizZk4Qkii5KWQLflfwfl0gkEom3hKIopTmgkiwiO2rLkSqaWi18rYlSWSatfM+XTCmAFK0UrHw/lO3Tlvl3D3v26AwSosI4Ud3Ee1uOsWSDcAf90kXHPVcMTo1hcEo0rRaVVfuE6OYUbK534wO+3FlEQ4uFnOQoJg1M8Gu83YGiKDywcAzjB8Tz09mDmZKT5Nd+wowGbtA6AL6w+iBWqxrMYXYpUpTqAVbvF4LU6Mw4clKie3g0XYQxDGI0xbd4h2iRCvZlEolE0suYM2cOd9xxR08PQ+KI7pRSAxelJBKJRCLxmkStdKzyMLQ0QJ3W1CkEyvdM9UVkxIbTYrGyZEMBRysaMZsMxLWUauv5VnZHsiZAle13CDn3T5SKCDNy8UQhnt33/g6a26xMyEpg5tAUr/ehu6WWa2IbSUNg6Fkw9Ex7uSHwntZ1b+HE/iE/8ZQeF8GHv5jJ784dGdB+rpw6kNgIEwdL621iZG9AilI9wLf7xAFhTm5ww9ZCjnitFvb4ZvEYmShapkokEkkXoiiKx6/rr7/er/2+9957PPDAA0EZ47p16zAajZxzzjlB2d9JSxCdUhKJRCKReI2jU0p3SUXEi/udnkIr31MszZyZI7qv/XO5EJHmDIlD0TvUxfnqlNLDzvOhLN95mR9cNVWU8LVYrADcNm+oT6LRmSOFyeGbvSVYrCoYDHDN/+Cad8VzoLimibX5wkmli2AnAzHhJq45NRuAF1Yd7OHReI8UpboZi1VllSZKnT48rYdH08XoIXrHNFFK5klJJJJuoLCw0Pa1ePFi4uLinJb985//dFrf2wyhpKQkYmODkwH48ssv88tf/pI1a9ZQUFAQlH36S6/OUDJIUUoikUgkPUBCFqBAaz0c3ySW9aRLCsAUDlEil2lWP3Fur2oQj+flaKKPKQKifCwPS9ZK6xydUgGIUsPTY23ldGP6xzE317d74snZicRHhlHZ0MqWgkqX63yYdxyrKtbNTu6jlUlu+L8ZOZiNBjYdqXT7+wk1pCjVzWw/Xk1lQyux4SYmhnBta1CIa+eUknlSEomkG8jIyLB9xcfHoyiK7fumpiYSEhJ4++23mTNnDhEREfz3v/+lvLycq666igEDBhAVFcXYsWNZsmSJ037bl+/l5OTwt7/9jRtuuIHY2FgGDhzICy+80On46uvrefvtt/nZz37G+eefz6uvvtphnY8++ogpU6YQERFBSkoKixYtsr3W3NzMPffcQ1ZWFuHh4QwbNoyXXnoJgFdffZWEhASnfX3wwQdOM5B//vOfmTBhAi+//DKDBw8mPDwcVVX54osvmDlzJgkJCSQnJ3P++edz4MABp30dO3aMK6+8kqSkJKKjo5kyZQrff/89hw8fxmAwsGnTJqf1n3rqKbKzs1HVLso1cCzf66r3kEgkEomkPaZw+73OwZXisadFKYBYUZo3ObHRafGsdC1KJS7T94Bym1Nqv+i+B3ahyk9+c84IxmclcP9FY3wurTMZDczVKo7clajppXsnk0tKJy0uggvGi/+Dtzce7eHReIcUpbqZb7WA85nDUggz9vFfv16+p1tFpVNKIun1qKpKQ0tbj3wFU9j4zW9+w2233cbu3bs5++yzaWpqYvLkyXzyySfs2LGDn/70p1x77bV8//33Hvfz2GOPMWXKFLZu3cqtt97Kz372M/bs2eNxm6VLl5Kbm0tubi7XXHMNr7zyitPP9umnn7Jo0SLOO+88tm7dyvLly5kyZYrt9euuu4633nqLJ598kt27d/Pcc88RExPj08+fn5/P22+/zbvvvkteXh4gxLK77rqLjRs3snz5cgwGAxdffDFWq7DX19XVcfrpp3PixAk++ugjtm3bxj333IPVaiUnJ4czzzyTV155xel9XnnlFa6//vquy3JQHNodq9aueQ+JRCKRSFyhi1AHv3X+vifRcqVSrBUMTBJdZccPiCepTQsFj/NDpIkbAKZI+3k2rj+YA3MfTRuczIc/P41JA/0rd5ynlfAt313S4bXdhTXsKarFbDRw/riT8/7zsimiRPPTHwppag19N7mppwdwsrFyn/jgnD68j+dJQceDnnRKSSS9nsZWC6P++GWPvPeu+88myhyc09Ydd9zh5D4CuPvuu23Pf/nLX/LFF1/wzjvvMG3aNLf7Offcc7n11lsBIXQ98cQTrFy5khEjRrjd5qWXXuKaa64B4JxzzqGuro7ly5dz5plnAvDXv/6VK6+8kr/85S+2bcaPF91k9u3bx9tvv82yZcts6w8ePNiXHx2AlpYWXn/9dVJT7eeiSy65pMM409LS2LVrF2PGjOHNN9+ktLSUjRs3kpQkrP9Dh9pnSm+66SZuueUWHn/8ccLDw9m2bRt5eXm89957Po/PaxQDoACqKOEzGDvbok/xzDPP8Oijj1JYWMjo0aNZvHgxs2bNcrv+G2+8wSOPPML+/fuJj4/nnHPO4R//+AfJyZ234ZZIJBJJOxJz4Mga0Wlc/76n0U0AtYWcMWIKr647zLlj+0HNRrHcH1HKYIDkIaJ5FQTskgoGp+emYjIo5JfUcaS83qlE7/2twiV1xog0EqLMPTXEHmVqThL9EyI5XtXIV7uKuXC8j+H23Uwft+qEFpX1LWw7WgWID1Kfp/1BL0aKUhKJJDRwdB4BWCwW/vrXvzJu3DiSk5OJiYnhq6++6jTvadw4extivUywpKTjrJ3O3r172bBhA1deeSUAJpOJK664gpdfftm2Tl5eHvPmzXO5fV5eHkajkdNPP73Tn9ET2dnZToIUwIEDB7j66qsZPHgwcXFxDBokOgvpv4O8vDwmTpxoE6Tas3DhQkwmE++//z4gcrPmzp1LTk5OQGP1iKKctB34li5dyh133MF9993H1q1bmTVrFgsWLHD7P7tmzRquu+46brzxRnbu3Mk777zDxo0buemmm7p55BKJRNJHaC9ChYIopXfWqznBr8/O5YVrJ3PjzEFQc8L5dV9xFKICyJMKFnERYUwdJK5HvnZwS1msKh9ootTFk06+0j0dg0HhEu3nf3fzsR4eTedIp1Q3sia/DKsKuemx9IuP7OnhdD3x0iklkfQ1IsOM7Lr/7B5772ARHe1sO3/sscd44oknWLx4MWPHjiU6Opo77riDlpYWj/sJCwtz+l5RFFu5myteeukl2tra6N/ffnxUVZWwsDAqKytJTEwkMtL9+cHTawAGg6FDmaOrIPP2Pz/ABRdcQFZWFi+++CKZmZlYrVbGjBlj+x109t5ms5lrr72WV155hUWLFvHmm2+yePFij9sEBcUItJ10YeePP/44N954o01UWrx4MV9++SXPPvssDz30UIf1v/vuO3JycrjtttsAGDRoEDfffDOPPPJIt45bIpFI+gyhKEo5OKWiw03MH63df1ULoabD/Zm3OApRKcP9H18QmTcynXUHylm+u1gIb8Da/DJKaptJiArzOUC9r3HxpAE8uSKf1ftLKalpIi0uoqeH5BbplOpGvtW77p0MLikQzijF4V9MZkpJJL0eRVGIMpt65KvLcomA1atXc9FFF3HNNdcwfvx4Bg8ezP79+4P6Hm1tbbz22ms89thj5OXl2b62bdtGdnY2b7zxBiDcV8uXL3e5j7Fjx2K1Wvn2229dvp6amkptbS319fW2ZXpmlCfKy8vZvXs3v//975k3bx4jR46kstK5Y8u4cePIy8ujoqLC7X5uuukmvv76a5555hlaW1s7lEh2CSehU6qlpYXNmzczf/58p+Xz589n3bp1LreZMWMGx44d47PPPkNVVYqLi/nf//7Heeed5/Z9mpubqampcfqSSCQSiUbSIPtzxQDxWT03Fh2bU6rQeXmNJkr5U74HkOwgSoVA+R7AmSOF6LThUAU1TWICTi/dO39cP8ymk1vqGJQSzeTsRKwqfJB3vKeH45Ee/0s988wzDBo0iIiICCZPnszq1avdrrtmzRpOO+00kpOTiYyMZMSIETzxxBPdOFr/sVpVuyh1MuRJARhNziV70iklkUhClKFDh7Js2TLWrVvH7t27ufnmmykqKgrqe3zyySdUVlZy4403MmbMGKevSy+91NZB709/+hNLlizhT3/6E7t372b79u02N0tOTg4//vGPueGGG/jggw84dOgQK1eu5O233wZg2rRpREVF8bvf/Y78/HzefPNNl9392pOYmEhycjIvvPAC+fn5rFixgrvuustpnauuuoqMjAwWLlzI2rVrOXjwIO+++y7r16+3rTNy5EhOPfVUfvOb33DVVVd16q4KCnrY+UnklCorK8NisZCenu60PD093e3/7YwZM3jjjTe44oorMJvNZGRkkJCQwFNPPeX2fR566CHi4+NtX1lZIXDDJZFIJKGCozMqfgAYw9yu2m3YnFInnJcHKkqlhFb5HkB2cjRD02Jos6p8u7eU+uY2vtghzoEXTxzQw6MLDRbZSviOd10n5CDQo6KUr3kI0dHR/OIXv2DVqlW2Gd3f//73XrXg7ml2F9VQWttMlNnIlBz/ugz0ShwtojHp7teTSCSSHuQPf/gDkyZN4uyzz2bOnDk28SWYvPTSS5x55pnEx8d3eO2SSy4hLy+PLVu2MGfOHN555x0++ugjJkyYwBlnnOHUBfDZZ5/l0ksv5dZbb2XEiBH85Cc/sTmjkpKS+O9//8tnn33G2LFjWbJkCX/+8587HZvBYOCtt95i8+bNjBkzhjvvvJNHH33UaR2z2cxXX31FWloa5557LmPHjuXhhx/GaHQuq7zxxhtpaWnhhhtu8OO35AeGk0+U0mnvHlRV1a2jcNeuXdx222388Y9/ZPPmzXzxxRccOnSIW265xe3+7733Xqqrq21fR4/2jtbSEolE0i1EJYNZ634bCqV7YHdKNZRDW7N43tYM9aXa636KUqkjICJedOKLCx3BZ57mllq+u5gvdxbR2GohJzmKSQMTenZgIcL5YzMxmwzsLa5l54nQdTsrag9KZtOmTWPSpEk8++yztmUjR45k4cKFLvMQXLFo0SKio6N5/fXXvVq/pqaG+Ph4qquriYuL82vc/vDMynwe+WIvZ45M498/PqXb3rfHefs62PUhRKX8f3t3HpZVnf9//HWzyA4iLsBYouLyQxG/ihamuZaaOa7jMi6YlpXCaNlYZo7aZaOVZZlGVyVqjRPFpF5OM6aYS+QyUYqhkTmKWophNimLgMH5/YHceYtrwjm38Hxc133Jfc65z3nf58MNb998Fmn6IaujAXCDCgsLlZWVZe/RClzLc889p6SkJGVkZJhzwZ+PlSXffiE31SP3at/rVuUOV1JcXCxvb28lJydr0KBB9u1TpkxRenr6ZYd3jhkzRoWFhUpOTrZv++yzz9SlSxedOHFCISHXHmLvbPcBACyXcFfZqnTtxkq/v3LPU9MYhjSvgVRSJE3ZW1Ys+ylLWtxWcvOUZp4sWyTktzibLbnWknycZ8XWtCM/6Q9v7FSAl7siQvy18/BpPdaruab0co7eXM5g8qrd+ldGth64K0yz+7cy9drXmzdY1lPqt8yHcKk9e/Zox44dN70KkRm2HqhhQ/fKlVfSGboHANVaXl6e0tLS9Nprr9kn0zZFDRy+V6tWLbVv314pKSkO21NSUtSpU6fLvqagoEAuLo5pX3kvN2fu0g8ATq28h1Rg46seZhqbTfK/8EeG8nmlLl5572bm5/QPcaqClCS1uz1Qgd7uOnPuvHYePi1JGvR/NXfVvcsZ0r7sfqxLP6HzJVdejMdKlhWlfst8COUaNmwoDw8PRUdHa/LkyVddztgZJuk8W3heu4+WTRjbtXkNWwWgfPgeRSkAqNbi4uLUuXNnde3a1byhe1KNnOhckh5//HG9/fbbSkxMVGZmph577DEdO3bMPhxvxowZGjt2rP34/v37a/Xq1UpISNDhw4e1fft2/elPf1LHjh0VGvoblwgHgJrurqlS5DApaoTVkfzK78LP9PJ5pW52Pikn5upic1hlL7pRoG4P8rYwIufTpVk91fWtpdP5xdp2oaOMs3GzOoAbmQ+hXGpqqvLy8rRr1y499dRTCg8P18iRIy977Pz58zV37txKi/e32PHf0/ql1FCTuj4170MSMUA6mCJ1nGh1JACAKrRixYrrmlS90rm6lw1JcLE8pTHV8OHDdfr0aT377LPKzs5W69at9e9//1uNGjWSJGVnZzvM0Tlu3Djl5uZqyZIlmjZtmmrXrq0ePXro+eeft+otAMCt77YOZQ9nUqGnVPUtSklSz//XQKsvrLo3qF31fI83w93VRQPa/k7LPsvS6j3fq1eE883zbFkGV7duXbm6ulboFZWTk1Oh99SlGjcu6x4ZGRmpH374QXPmzLliUWrGjBkOKwidPXvW9NVjtn2bI0m6u6YN3ZPKVqIYu9bqKAAA1ZV3UNmjBpo0aZImTZp02X2XKxDGx8crPj6+iqMCAFjKvgLfhaLUmQtFqYDqWbC5u3ld+Xu6yVDZxN6oaHC7sqLUpq9z9HNBsWp717I6JAeWDd/7LfMhXI5hGCoqKrrifg8PD/n7+zs8zGQYhr2bXNcWNbAoBQAAAAAwR/kKfOVzSV08p1Q15Ofprn/Gd9ZH8Z0V4O1udThOqVVogFoG+6m4pFQffZVtdTgVWNrX/fHHH9eYMWMUHR2tmJgYvfnmmxXmQzh+/LjeeecdSdLSpUt1++23q2XLlpLKVo1ZuHChU//V7785eTpxplAebi6KaVIz/5ILAAAAADBBeU8pe1Hq+7J/yxegqoYaBflYHYLTG9q+oeb9K1Mf7v5eo+9sZHU4DiwtSt3ofAilpaWaMWOGsrKy5ObmpqZNm2rBggV6+OGHrXoL11S+6t4dTYLk6e5qcTQAAAAAgGrL/9KJzqt3Tylcn9+3DdX89d9oz7GfdehUnprW87U6JDvLZwW9kfkQbsW5ELZ9e2HoXk2cTwoAAAAAYB77nFInpfOFUv6FFdcCqm9PKVxbfT9P3d2srrYcOKU1u4/rid4trA7JzrI5pWqCguJf9HnWT5KkbswnBQAAAACoSuVFqZJi6Yd9ZV+7eUpegdbFBKcwuF1ZYXLNnuMqLTUsjuZXFKWq0K7Dp1VcUqqGgV5qUpdxrgBwK+nWrZumTp1qfx4WFqZXXnnlqq+x2Wxau3btTV+7ss4DAABqGLdaks+FDhHff1H2r//vJJvNupjgFO6JaCA/Tzcd//mcdmWdtjocO4pSVah8PqmuzevJxg8BADBF//791atXr8vu27lzp2w2m3bv3n3D501LS9PEiRNvNjwHc+bMUdu2bStsz87OVt++fSv1Wldy7tw5BQYGqk6dOjp37pwp1wQAAFWovLfU92ll/zKfFCR5urvq/jZl3xsffnnc4mh+RVGqCpXPJ9WtRX2LIwGAmmPChAnavHmzjh49WmFfYmKi2rZtq3bt2t3weevVqydvb+/KCPGagoOD5eHhYcq1PvzwQ7Vu3VoRERFavXq1Kde8EsMw9Msvv1gaAwAAt7zyItTxCz2lmE8KFwy5MIRv/b5sFRQ7R85FUaqKHPkxX0dPF8jd1aaYpkFWhwMANcb999+v+vXrV1gso6CgQO+//74mTJig06dPa+TIkWrYsKG8vb0VGRmp995776rnvXT43sGDB3X33XfL09NTERERSklJqfCaJ598Us2bN5e3t7eaNGmiWbNm6fz585LKFvOYO3eu9u7dK5vNJpvNZo/50uF7GRkZ6tGjh7y8vBQUFKSJEycqLy/Pvn/cuHEaOHCgFi5cqJCQEAUFBWny5Mn2a13NsmXLNHr0aI0ePVrLli2rsH///v3q16+f/P395efnpy5duujQoUP2/YmJiWrVqpU8PDwUEhKiuLg4SdKRI0dks9mUnp5uP/bnn3+WzWbT1q1bJUlbt26VzWbThg0bFB0dLQ8PD6WmpurQoUMaMGCAGjRoIF9fX3Xo0EGbNm1yiKuoqEjTp0/XbbfdJg8PDzVr1kzLli2TYRgKDw/XwoULHY7ft2+fXFxcHGIHAKBaKu8p9b8jZf/SUwoXtG8UqEZB3iooLtHH+05aHY4kilJVZuuBHElSdKM68vWwfJFDAKgchiEV51vzMK5vQkY3NzeNHTtWK1askHHRa5KTk1VcXKxRo0apsLBQ7du310cffaR9+/Zp4sSJGjNmjP7zn/9c1zVKS0s1ePBgubq6ateuXXrjjTf05JNPVjjOz89PK1as0Ndff61XX31Vb731lhYtWiRJGj58uKZNm6ZWrVopOztb2dnZGj58eIVzFBQUqE+fPgoMDFRaWpqSk5O1adMme/Gn3JYtW3To0CFt2bJFK1eu1IoVKyoU5i516NAh7dy5U8OGDdOwYcO0Y8cOHT582L7/+PHj9sLb5s2b9eWXX2r8+PH23kwJCQmaPHmyJk6cqIyMDK1bt07h4eHXdQ8vNn36dM2fP1+ZmZlq06aN8vLydN9992nTpk3as2ePevfurf79++vYsWP214wdO1ZJSUlavHixMjMz9cYbb8jX11c2m03jx4/X8uXLHa6RmJioLl26qGnTpjccHwAAt5RLi1D+v7MmDjgdm82mwf9X1ltq9W7nGMJHtaSK/Dp0j1X3AFQj5wukv1r017anT0i1rm/RiPHjx+vFF1/U1q1b1b17d0llRYnBgwcrMDBQgYGBeuKJJ+zHx8fH6+OPP1ZycrLuuOOOa55/06ZNyszM1JEjR9SwYdkv9r/+9a8V5oF65pln7F+HhYVp2rRpev/99zV9+nR5eXnJ19dXbm5uCg4OvuK1Vq1apXPnzumdd96Rj0/Z+1+yZIn69++v559/Xg0aNJAkBQYGasmSJXJ1dVXLli3Vr18/ffLJJ3rooYeueO7ExET17dtXgYFlK/L06dNHiYmJmjdvniRp6dKlCggIUFJSktzd3SVJzZs3t79+3rx5mjZtmqZMmWLf1qFDh2vev0s9++yzuueee+zPg4KCFBUV5XCdNWvWaN26dYqLi9O3336rDz74QCkpKfb5w5o0aWI//oEHHtBf/vIXff755+rYsaPOnz+vv/3tb3rxxRdvODYAAG455T2lylGUwkUGt/udFm36VtsP/agTP59TaG0vS+Ohp1QVKDxfop2Hy2az70pRCgBM17JlS3Xq1EmJiYmSynoEpaamavz48ZKkkpISPffcc2rTpo2CgoLk6+urjRs3OvTEuZrMzEzdfvvt9oKUJMXExFQ47h//+Ic6d+6s4OBg+fr6atasWdd9jYuvFRUVZS9ISdJdd92l0tJSHThwwL6tVatWcnV1tT8PCQlRTk7OFc9bUlKilStXavTo0fZto0eP1sqVK1VSUiJJSk9PV5cuXewFqYvl5OToxIkT6tmz5w29n8uJjo52eJ6fn6/p06crIiJCtWvXlq+vr7755hv7vUtPT5erq6u6du162fOFhISoX79+9vb/6KOPVFhYqD/84Q83HSsAAE7P/5KiVABFKfzqtjre6ti4jgxDWrPH+t5S9JSqAp9n/aTC86UK9vdUiwZ+VocDAJXH3busx5JV174BEyZMUFxcnJYuXarly5erUaNG9gLKSy+9pEWLFumVV15RZGSkfHx8NHXqVBUXF1/XuY3LDCW8dJXVXbt2acSIEZo7d6569+5t73H00ksv3dD7MAzjiiu4Xrz90sKRzWZTaWnpFc+7YcMGHT9+vMKQwZKSEm3cuFF9+/aVl9eV/3J2tX2S5OLiYo+/3JXmuLq44CZJf/7zn7VhwwYtXLhQ4eHh8vLy0tChQ+3tc61rS9KDDz6oMWPGaNGiRVq+fLmGDx9u2kT1AABYyo/he7i6Ie1+p8+zftLq3d9rUremV8w1zUBPqSpQPnSva/N6ljYuAFQ6m61sCJ0Vjxv8eTps2DC5urrq73//u1auXKkHHnjA/jM5NTVVAwYM0OjRoxUVFaUmTZro4MGD133uiIgIHTt2TCdO/Fqg27lzp8Mx27dvV6NGjTRz5kxFR0erWbNmFVYErFWrlr1X0tWulZ6ervz8fIdzu7i4OAylu1HLli3TiBEjlJ6e7vAYNWqUfcLzNm3aKDU19bLFJD8/P4WFhemTTz657Pnr1SvrKZydnW3fdvGk51eTmpqqcePGadCgQYqMjFRwcLCOHDli3x8ZGanS0lJt27btiue477775OPjo4SEBK1fv97eSw4AgGrv4p5Sbl6SV6B1scAp3RcZIg83Fx06la+935+xNBaKUlXAXpRi6B4AWMbX11fDhw/X008/rRMnTmjcuHH2feHh4UpJSdGOHTuUmZmphx9+WCdPXv8KJL169VKLFi00duxY7d27V6mpqZo5c6bDMeHh4Tp27JiSkpJ06NAhLV68WGvWrHE4JiwsTFlZWUpPT9ePP/6ooqKiCtcaNWqUPD09FRsbq3379mnLli2Kj4/XmDFj7PNJ3ahTp07pn//8p2JjY9W6dWuHR2xsrNatW6dTp04pLi5OZ8+e1YgRI/TFF1/o4MGDevfdd+3DBufMmaOXXnpJixcv1sGDB7V792699tprksp6M915551asGCBvv76a3366acOc2xdTXh4uFavXq309HTt3btXf/zjHx16fYWFhSk2Nlbjx4/X2rVrlZWVpa1bt+qDDz6wH+Pq6qpx48ZpxowZCg8Pv+zwSgAAqiXP2mXFKKls0nM6SuASfp7uerRbU839fSuFBVnbk5yiVCX7/n8F+m9OnlxdbLorvK7V4QBAjTZhwgT973//U69evXT77bfbt8+aNUvt2rVT79691a1bNwUHB2vgwIHXfV4XFxetWbNGRUVF6tixox588EE999xzDscMGDBAjz32mOLi4tS2bVvt2LFDs2bNcjhmyJAh6tOnj7p376569erpvffeq3Atb29vbdiwQT/99JM6dOigoUOHqmfPnlqyZMmN3YyLlE+afrn5oLp37y4/Pz+9++67CgoK0ubNm5WXl6euXbuqffv2euutt+xDBWNjY/XKK6/o9ddfV6tWrXT//fc79DhLTEzU+fPnFR0drSlTptgnUL+WRYsWKTAwUJ06dVL//v3Vu3dvtWvXzuGYhIQEDR06VJMmTVLLli310EMPOfQmk8rav7i4mF5SAICaxWb7tbcU80nhCqb2aq7YTmGq7V3L0jhsxuUmxqjGzp49q4CAAJ05c0b+/v6Vfv5V/zmqmWv2qUNYoJIf6VTp5wcAMxUWFiorK0uNGzeWp6en1eEAN2T79u3q1q2bvv/++2v2Krva93pV5w63Cu4DANxClveTjn4mRY2UBr1hdTSoga43b2Ci80q27cCv80kBAADzFRUV6bvvvtOsWbM0bNiw3zzMEQCAW5b/hcnOmeQcTo7he5UsyNdDgd7u6tq8vtWhAABQI7333ntq0aKFzpw5oxdeeMHqcAAAMF/7WCmsixT5B6sjAa6K4XtVoKTUkIut4vLgAHCrYfgeagqG710b9wEAAFwvhu9ZyNWFYhQAAAAAAMDVMHwPAAAAAAAApqMoBQC4pho20hs1EN/jAAAA5qMoBQC4Ind3d0lSQUGBxZEAVav8e7z8ex4AAABVjzmlAABX5Orqqtq1aysnJ0eS5O3tzSIOqFYMw1BBQYFycnJUu3Ztubq6Wh0SAABAjUFRCgBwVcHBwZJkL0wB1VHt2rXt3+sAAAAwB0UpAMBV2Ww2hYSEqH79+jp//rzV4QCVzt3dnR5SAAAAFqAoBQC4Lq6urvzHHQAAAEClYaJzAAAAAAAAmI6iFAAAAAAAAExHUQoAAAAAAACmq3FzShmGIUk6e/asxZEAAIBbQXnOUJ5D1FTkUAAA4Hpdb/5U44pSubm5kqTbbrvN4kgAAMCtJDc3VwEBAVaHYRlyKAAAcKOulT/ZjBr2Z7/S0lKdOHFCfn5+stlslX7+s2fP6rbbbtN3330nf3//Sj8/rg/t4BxoB+vRBs6BdrDezbSBYRjKzc1VaGioXFxq7swH5FDVH23gHGgH50A7WI82cA6/tR2uN3+qcT2lXFxc1LBhwyq/jr+/Px8cJ0A7OAfawXq0gXOgHaz3W9ugJveQKkcOVXPQBs6BdnAOtIP1aAPn8Fva4Xryp5r75z4AAAAAAABYhqIUAAAAAAAATEdRqpJ5eHho9uzZ8vDwsDqUGo12cA60g/VoA+dAO1iPNnB+tJH1aAPnQDs4B9rBerSBc6jqdqhxE50DAAAAAADAevSUAgAAAAAAgOkoSgEAAAAAAMB0FKUAAAAAAABgOopSlez1119X48aN5enpqfbt2ys1NdXqkKq1Tz/9VP3791doaKhsNpvWrl3rsN8wDM2ZM0ehoaHy8vJSt27dtH//fmuCrabmz5+vDh06yM/PT/Xr19fAgQN14MABh2Noh6qVkJCgNm3ayN/fX/7+/oqJidH69evt+7n/1pg/f75sNpumTp1q30ZbVL05c+bIZrM5PIKDg+37aQPnRP5kLvIn65E/OQdyKOdD/mQNK/MnilKV6P3339fUqVM1c+ZM7dmzR126dFHfvn117Ngxq0OrtvLz8xUVFaUlS5Zcdv8LL7ygl19+WUuWLFFaWpqCg4N1zz33KDc31+RIq69t27Zp8uTJ2rVrl1JSUvTLL7/o3nvvVX5+vv0Y2qFqNWzYUAsWLNAXX3yhL774Qj169NCAAQPsvyi4/+ZLS0vTm2++qTZt2jhspy3M0apVK2VnZ9sfGRkZ9n20gfMhfzIf+ZP1yJ+cAzmUcyF/spZl+ZOBStOxY0fjkUcecdjWsmVL46mnnrIooppFkrFmzRr789LSUiM4ONhYsGCBfVt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+      "text/plain": [
+       "<Figure size 1200x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot accuracy graph\n",
+    "history = emotion_model_info.history\n",
+    "\n",
+    "plt.figure(figsize=(12, 4))\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.plot(history['accuracy'], label='Train Accuracy')\n",
+    "plt.plot(history['val_accuracy'], label='Validation Accuracy')\n",
+    "plt.title('Model Accuracy')\n",
+    "plt.ylabel('Accuracy')\n",
+    "plt.xlabel('Epoch')\n",
+    "plt.legend(loc='lower right')\n",
+    "\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.plot(history['loss'], label='Train Loss')\n",
+    "plt.plot(history['val_loss'], label='Validation Loss')\n",
+    "plt.title('Model Loss')\n",
+    "plt.ylabel('Loss')\n",
+    "plt.xlabel('Epoch')\n",
+    "plt.legend(loc='upper right')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "f2af2802",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
+     ]
+    }
+   ],
+   "source": [
+    "emotion_model.save('final_train.h5')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ab652b03",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/haarcascade_frontalface_default.xml b/haarcascade_frontalface_default.xml
new file mode 100644
index 0000000..cbd1aa8
--- /dev/null
+++ b/haarcascade_frontalface_default.xml
@@ -0,0 +1,33314 @@
+<?xml version="1.0"?>
+<!--
+    Stump-based 24x24 discrete(?) adaboost frontal face detector.
+    Created by Rainer Lienhart.
+
+////////////////////////////////////////////////////////////////////////////////////////
+
+  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+
+  By downloading, copying, installing or using the software you agree to this license.
+  If you do not agree to this license, do not download, install,
+  copy or use the software.
+
+
+                        Intel License Agreement
+                For Open Source Computer Vision Library
+
+ Copyright (C) 2000, Intel Corporation, all rights reserved.
+ Third party copyrights are property of their respective owners.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+   * Redistribution's of source code must retain the above copyright notice,
+     this list of conditions and the following disclaimer.
+
+   * Redistribution's in binary form must reproduce the above copyright notice,
+     this list of conditions and the following disclaimer in the documentation
+     and/or other materials provided with the distribution.
+
+   * The name of Intel Corporation may not be used to endorse or promote products
+     derived from this software without specific prior written permission.
+
+ This software is provided by the copyright holders and contributors "as is" and
+ any express or implied warranties, including, but not limited to, the implied
+ warranties of merchantability and fitness for a particular purpose are disclaimed.
+ In no event shall the Intel Corporation or contributors be liable for any direct,
+ indirect, incidental, special, exemplary, or consequential damages
+ (including, but not limited to, procurement of substitute goods or services;
+ loss of use, data, or profits; or business interruption) however caused
+ and on any theory of liability, whether in contract, strict liability,
+ or tort (including negligence or otherwise) arising in any way out of
+ the use of this software, even if advised of the possibility of such damage.
+-->
+<opencv_storage>
+<cascade type_id="opencv-cascade-classifier"><stageType>BOOST</stageType>
+  <featureType>HAAR</featureType>
+  <height>24</height>
+  <width>24</width>
+  <stageParams>
+    <maxWeakCount>211</maxWeakCount></stageParams>
+  <featureParams>
+    <maxCatCount>0</maxCatCount></featureParams>
+  <stageNum>25</stageNum>
+  <stages>
+    <_>
+      <maxWeakCount>9</maxWeakCount>
+      <stageThreshold>-5.0425500869750977e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 0 -3.1511999666690826e-02</internalNodes>
+          <leafValues>
+            2.0875380039215088e+00 -2.2172100543975830e+00</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 1 1.2396000325679779e-02</internalNodes>
+          <leafValues>
+            -1.8633940219879150e+00 1.3272049427032471e+00</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 2 2.1927999332547188e-02</internalNodes>
+          <leafValues>
+            -1.5105249881744385e+00 1.0625729560852051e+00</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 3 5.7529998011887074e-03</internalNodes>
+          <leafValues>
+            -8.7463897466659546e-01 1.1760339736938477e+00</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 4 1.5014000236988068e-02</internalNodes>
+          <leafValues>
+            -7.7945697307586670e-01 1.2608419656753540e+00</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 5 9.9371001124382019e-02</internalNodes>
+          <leafValues>
+            5.5751299858093262e-01 -1.8743000030517578e+00</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 6 2.7340000960975885e-03</internalNodes>
+          <leafValues>
+            -1.6911929845809937e+00 4.4009700417518616e-01</leafValues></_>
+        <_>
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