tf.image.extract_patches error for tf.RaggedTensor inputs
Hi @RaminNateghi ,

The reason for this behaviour is not all Ops supports Ragged Tensors. You can find the list of supported Ops [here](https://www.tensorflow.org/api_docs/python/tf/ragged). For `tf.image` module only the following Ops support Ragged Tensors.

> [tf.image.adjust_brightness](https://www.tensorflow.org/api_docs/python/tf/image/adjust_brightness)(image, delta)
> [tf.image.adjust_gamma](https://www.tensorflow.org/api_docs/python/tf/image/adjust_gamma)(image, gamma=1, gain=1)
> [tf.image.convert_image_dtype](https://www.tensorflow.org/api_docs/python/tf/image/convert_image_dtype)(image, dtype, saturate=False, name=None)
> [tf.image.random_brightness](https://www.tensorflow.org/api_docs/python/tf/image/random_brightness)(image, max_delta, seed=None)
> [tf.image.resize](https://www.tensorflow.org/api_docs/python/tf/image/resize)(images, size, method='bilinear', preserve_aspect_ratio=False, antialias=False, name=None)
> [tf.image.stateless_random_brightness](https://www.tensorflow.org/api_docs/python/tf/image/stateless_random_brightness)(image, max_delta, seed)

Hope this will resolve your query. Thanks! This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you. @SuryanarayanaY, thank you for your explanation. Hi @RaminNateghi ,

Can we considered it as resolved now. Please feel free to close the issue if resolved for you.

Thanks! This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you. This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further. Are you satisfied with the resolution of your issue?
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### Issue type

Bug

### Have you reproduced the bug with TensorFlow Nightly?

Yes

### Source

source

### TensorFlow version

tf 2.12.0

### Custom code

Yes

### OS platform and distribution

_No response_

### Mobile device

_No response_

### Python version

_No response_

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

`tf.image.extract_patches` should be able to extract patches from `ragged `tensors.

### Standalone code to reproduce the issue

```shell
def build_model():
  input = tf.keras.Input([None, None, 3], ragged=True, name="image")
  patches = tf.image.extract_patches(
              images=input,
              sizes=[1, 4, 4, 1],
              strides=[1, 4, 4, 1],
              rates=[1, 1, 1, 1],
              padding="SAME",
          )
  
  return tf.keras.Model(
            inputs=input,
            outputs=patches)
  

model = build_model()
```


### Relevant log output

```shell
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-48-0f2eac36aeae> in <cell line: 16>()
     14 
     15 
---> 16 model = build_model()

3 frames
/usr/local/lib/python3.10/dist-packages/keras/utils/traceback_utils.py in error_handler(*args, **kwargs)
     68             # To get the full stack trace, call:
     69             # `tf.debugging.disable_traceback_filtering()`
---> 70             raise e.with_traceback(filtered_tb) from None
     71         finally:
     72             del filtered_tb

TypeError: Exception encountered when calling layer "tf.image.extract_patches_3" (type TFOpLambda).

Failed to convert elements of tf.RaggedTensor(values=tf.RaggedTensor(values=Tensor("Placeholder:0", shape=(None, 3), dtype=float32), row_splits=Tensor("Placeholder_1:0", shape=(None,), dtype=int64)), row_splits=Tensor("Placeholder_2:0", shape=(None,), dtype=int64)) to Tensor. Consider casting elements to a supported type. See https://www.tensorflow.org/api_docs/python/tf/dtypes for supported TF dtypes.

Call arguments received by layer "tf.image.extract_patches_3" (type TFOpLambda):
  • images=tf.RaggedTensor(values=tf.RaggedTensor(values=Tensor("Placeholder:0", shape=(None, 3), dtype=float32), row_splits=Tensor("Placeholder_1:0", shape=(None,), dtype=int64)), row_splits=Tensor("Placeholder_2:0", shape=(None,), dtype=int64))
  • sizes=['1', '4', '4', '1']
  • strides=['1', '4', '4', '1']
  • rates=['1', '1', '1', '1']
  • padding='SAME'
  • name=None
```
