Make ops.abs and ops.absolute consistent between backends. (#19563)

- The TensorFlow implementation was missing `convert_to_tensor`
- The sparse annotation was unnecessarily applied twice
- Now `abs` calls `absolute` in all backends

Also fixed TensorFlow `ops.select`.

keras/src/backend/jax/numpy.py

@@ -223,10 +223,8 @@ def absolute(x):
     return jnp.absolute(x)
 
 
-@sparse.elementwise_unary(linear=False)
 def abs(x):
-    x = convert_to_tensor(x)
-    return jnp.absolute(x)
+    return absolute(x)
 
 
 def all(x, axis=None, keepdims=False):

keras/src/backend/tensorflow/numpy.py

@@ -615,14 +615,14 @@ def zeros(shape, dtype=None):
 
 @sparse.elementwise_unary
 def absolute(x):
+    x = convert_to_tensor(x)
     # uintx and bool are always non-negative
     dtype = standardize_dtype(x.dtype)
     if "uint" in dtype or dtype == "bool":
         return x
     return tf.abs(x)
 
 
-@sparse.elementwise_unary
 def abs(x):
     return absolute(x)
 
@@ -2405,4 +2405,4 @@ def correlate(x1, x2, mode="valid"):
 
 
 def select(condlist, choicelist, default=0):
-    return tfnp.select(condlist, choicelist, default=default)
+    return tf.experimental.numpy.select(condlist, choicelist, default=default)

keras/src/backend/torch/numpy.py

@@ -201,17 +201,17 @@ def zeros_like(x, dtype=None):
 
 
 def absolute(x):
-    return abs(x)
-
-
-def abs(x):
     x = convert_to_tensor(x)
     # bool are always non-negative
     if standardize_dtype(x.dtype) == "bool":
         return x
     return torch.abs(x)
 
 
+def abs(x):
+    return absolute(x)
+
+
 def all(x, axis=None, keepdims=False):
     x = convert_to_tensor(x)
     if axis is None: