import tensorflow as tf
import numpy as np
import os


os.environ['CUDA_VISIBLE_DEVICES'] = ''

def Model_HqTi1yjRFc7Dz1RLSJOvA9XX16R5Wp01(x):
    x = tf.keras.Input(shape=x)
    _x = x
    _zeropadding_x = tf.keras.layers.ZeroPadding1D(padding=(0, 0))(x)
    x = tf.keras.layers.Conv1D(filters=64, kernel_size=1, strides=1, padding="valid", data_format="channels_last", dilation_rate=1, groups=1, use_bias=True, name="conv1")(_zeropadding_x)
    x = tf.keras.layers.BatchNormalization(axis=-1, epsilon=1e-05, momentum=0.9, center=True, scale=True, name="bn1")(x)
    x = tf.nn.relu(x)
    _zeropadding_x = tf.keras.layers.ZeroPadding1D(padding=(0, 0))(x)
    x = tf.keras.layers.Conv1D(filters=64, kernel_size=1, strides=1, padding="valid", data_format="channels_last", dilation_rate=1, groups=1, use_bias=True, name="conv2")(_zeropadding_x)
    x = tf.keras.layers.BatchNormalization(axis=-1, epsilon=1e-05, momentum=0.9, center=True, scale=True, name="bn2")(x)
    x = tf.nn.relu(x)
    _zeropadding_x = tf.keras.layers.ZeroPadding1D(padding=(0, 0))(x)
    x = tf.keras.layers.Conv1D(filters=64, kernel_size=1, strides=1, padding="valid", data_format="channels_last", dilation_rate=1, groups=1, use_bias=True, name="conv3")(_zeropadding_x)
    x = tf.keras.layers.BatchNormalization(axis=-1, epsilon=1e-05, momentum=0.9, center=True, scale=True, name="bn3")(x)
    x = tf.keras.activations.relu(x)
    _zeropadding_x = tf.keras.layers.ZeroPadding1D(padding=(0, 0))(x)
    x = tf.keras.layers.Conv1DTranspose(filters=128, kernel_size=1, strides=1, padding="valid", output_padding=0, data_format="channels_last", dilation_rate=8, use_bias=True, name="conv4_mutated")(x)

    x = x
    model = tf.keras.models.Model(inputs=_x, outputs=x)
    return model


def go():
    with tf.device('/CPU:0'):
        shape = [2, 3, 2048]
        _numpy = np.random.random(shape).astype(np.float32)
        tf_input = tf.convert_to_tensor(_numpy.transpose(0, 2, 1), dtype=tf.float32)
        tf_model = Model_HqTi1yjRFc7Dz1RLSJOvA9XX16R5Wp01(tf_input.shape[1:])
        tf_output = tf_model(tf_input)




go()