tf.image.extract_patches fails to compute gradients
The issue with **tf.image.extract_patches** failing to compute gradients is that the function is not differentiable with respect to the input. The **extract_patches** operation is not implemented in a way that allows for gradients to be propagated through it.

One workaround for this issue is to implement the patch extraction manually using TensorFlow's built-in operations that support gradients. Here is an example implementation that achieves the same result as **tf.image.extract_patches**:
```
import os
import tensorflow as tf
import numpy as np

x = tf.Variable(tf.ones([1, 5, 5, 1]), tf.float32)

def func(x):
    patches = tf.image.extract_image_patches(
        x,
        ksizes=[1, 3, 3, 1],
        strides=[1, 1, 1, 1],
        rates=[1, 1, 1, 1],
        padding='VALID'
    )
    return tf.reshape(patches, [-1, 3, 3, 1])

with tf.GradientTape(persistent=True) as tape:
    tape.watch(x)
    output_rev = func(x)
    print(output_rev)

gradient = tape.jacobian(output_rev, x)
```
In this implementation, **tf.image.extract_image_patches** is used instead of **tf.image.extract_patches**, as **tf.image.extract_image_patches** supports gradients. The output of **tf.image.extract_image_patches** is reshaped to match the output of **tf.image.extract_patches**. @trickiwoo While replicating the issue reported here , I faced the following log;
```
TypeError: Tensors in list passed to 'values' of 'ConcatV2' Op have types [int32, int64] that don't all match.
 Please find the [gist ]
```
Please find the gist [here](https://colab.research.google.com/gist/sushreebarsa/878146fc2898f7db64e6a2231998c52f/59979.ipynb) here.

Can we use tf.image.extract_image_patches instead for the implementation?
Thank you!  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?
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59979">Yes</a>
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<details><summary>Click to expand!</summary> 
 
 ### Issue Type

Bug

### Have you reproduced the bug with TF nightly?

Yes

### Source

source

### Tensorflow Version

2.11

### 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 Behaviour?

```shell
tf.image.extract_patches fails to compute gradients. A similar [issue](https://github.com/tensorflow/tensorflow/issues/7414) says it can only compute gradients with `float32` inputs, but it doesn't work. According to the logs output, the error isn't related to the input `x` types.
```


### Standalone code to reproduce the issue

```shell
import os
import tensorflow as tf
import numpy as np

x = tf.Variable(tf.ones([1, 5, 5, 1]), tf.float32)

def func(x):
    patches = tf.image.extract_patches(x, [1, 3, 3, 1], [1, 1, 1, 1], [1, 1, 1, 1], 'VALID')
    return patches

with tf.GradientTape(persistent=True) as tape:
    tape.watch(x)
    output_rev = func(x)
    print(output_rev)

gradient = tape.jacobian(output_rev, x)
```


### Relevant log output

```shell
ERROR:tensorflow:Got error while pfor was converting op name: "gradient_tape/Reshape_4"
op: "Reshape"
input: "gradient_tape/Reshape"
input: "gradient_tape/Reshape_4/shape"
attr {
  key: "T"
  value {
    type: DT_FLOAT
  }
}
attr {
  key: "Tshape"
  value {
    type: DT_INT64
  }
}
 with inputs (<tf.Tensor 'gradient_tape/Reshape:0' shape=(1, 3, 3, 9) dtype=float32>, <tf.Tensor 'gradient_tape/Reshape_4/shape:0' shape=(6,) dtype=int64>)
, converted inputs [WrappedTensor(t=<tf.Tensor 'gradient_tape/Reshape/pfor/Reshape:0' shape=(81, 1, 3, 3, 9) dtype=float32>, is_stacked=True, is_sparse_stacked=False), WrappedTensor(t=<tf.Tensor 'gradient_tape/Reshape_4/shape:0' shape=(6,) dtype=int64>, is_stacked=False, is_sparse_stacked=False)]
Here are the pfor conversion stack traces: Tensors in list passed to 'values' of 'ConcatV2' Op have types [int32, int64] that don't all match.
```
</details>