Fix code formatting

1. Fix line length issues in tensorflow/numpy.py
2. Remove extra blank lines
3. Use specific exception types instead of bare except
4. Improve docstring formatting

Author: ssam18

keras/src/backend/tensorflow/numpy.py

@@ -2742,7 +2742,7 @@ def round(x, decimals=0):
 
 def tile(x, repeats):
     x = convert_to_tensor(x)
-    
+
     # Check if repeats contains only concrete integers
     # If so, keep it as a Python list/tuple for better shape inference
     try:
@@ -2752,14 +2752,14 @@ def tile(x, repeats):
             for r in repeats:
                 if isinstance(r, int):
                     concrete_repeats.append(r)
-                elif hasattr(r, 'numpy') and r.shape == ():
+                elif hasattr(r, "numpy") and r.shape == ():
                     # Scalar tensor with concrete value
                     concrete_repeats.append(int(r.numpy()))
                 else:
                     # Not a concrete value, fall back to tensor path
                     concrete_repeats = None
                     break
-            
+
             if concrete_repeats is not None:
                 # Use concrete repeats directly for better shape inference
                 repeats = concrete_repeats
@@ -2770,13 +2770,18 @@ def tile(x, repeats):
                         repeats = [1] * (x_rank - len(repeats)) + repeats
                     elif len(repeats) > x_rank:
                         # Need to reshape x to match repeats length
-                        x_shape_list = [1] * (len(repeats) - x_rank) + [d if d is not None else -1 for d in x.shape.as_list()]
+                        x_shape_list = [1] * (
+                            len(repeats) - x_rank
+                        ) + [
+                            d if d is not None else -1
+                            for d in x.shape.as_list()
+                        ]
                         x = tf.reshape(x, x_shape_list)
                 return tf.tile(x, repeats)
-    except Exception:
+    except (AttributeError, TypeError, ValueError):
         # If anything goes wrong, fall back to original implementation
         pass
-    
+
     # Original dynamic implementation for non-concrete repeats
     repeats = tf.reshape(convert_to_tensor(repeats, dtype="int32"), [-1])
     repeats_size = tf.size(repeats)

keras/src/ops/numpy.py

@@ -6411,15 +6411,15 @@ def compute_output_spec(self, x):
         repeats = self.repeats
         if isinstance(repeats, int):
             repeats = [repeats]
-        
+
         # Convert repeats to list if it's a tuple or other iterable
         # and extract concrete integer values
         if not isinstance(repeats, list):
             try:
                 repeats = list(repeats)
             except TypeError:
                 repeats = [repeats]
-        
+
         if len(x_shape) > len(repeats):
             repeats = [1] * (len(x_shape) - len(repeats)) + repeats
         else:

keras/src/ops/numpy_test.py

@@ -1820,7 +1820,7 @@ def test_tile(self):
         self.assertEqual(knp.tile(x, [2]).shape, (None, 6))
         self.assertEqual(knp.tile(x, [1, 2]).shape, (None, 6))
         self.assertEqual(knp.tile(x, [2, 1, 2]).shape, (2, None, 6))
-        
+
         # Test with multi-dimensional input
         x = KerasTensor((None, 3, 2, 2))
         self.assertEqual(knp.tile(x, [1, 2, 1, 1]).shape, (None, 6, 2, 2))
@@ -9513,12 +9513,13 @@ def call(self, x):
         model.predict(np.random.randn(1, 8))
 
     def test_tile_shape_inference_in_layer(self):
-        """Test that ops.tile properly infers output shape when used in a Layer.
-        
+        """Test that ops.tile properly infers output shape in a Layer.
+
         This is a regression test for issue #20914 where TensorFlow backend
         would return all-None shapes when tile was called inside a Layer's
         call method with concrete integer repeats.
         """
+
         class TileLayer(keras.layers.Layer):
             def call(self, x):
                 # Use concrete integer repeats
@@ -9527,7 +9528,7 @@ def call(self, x):
 
         inputs = keras.Input(shape=(3, 2, 2))
         output = TileLayer()(inputs)
-        
+
         # With the fix, output shape should be (None, 6, 2, 2)
         # Before the fix, it was (None, None, None, None)
         self.assertEqual(output.shape, (None, 6, 2, 2))