Trying to reshape a tensor after applying a boolean_mask with XLA context will result in an error or wrong values
Hi **@MoritzMSP** ,
It throws an error as it should, given that you're attempting to reshape the tensor to [3, 2], a shape that does not match the number of elements passed through the mask (which should be 6 for your example). This is the correct behavior because XLA cannot validate the shape you're trying to reshape into due to the dynamic nature of the boolean mask operation.
Here I attached [gist](https://colab.sandbox.google.com/gist/Venkat6871/5850f413a403c2ad3bf8c934bfd66030/62878_2-15-v.ipynb) for your reference.

Thank you!

 @Venkat6871  thank you for clarification 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/62878">Yes</a>
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62878">No</a>

### Issue type

Bug

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

No

### Source

source

### TensorFlow version

2.15.post1

### Custom code

Yes

### OS platform and distribution

WSL Ubuntu 22.04

### Mobile device

_No response_

### Python version

3.10.12

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

Trying to reshape a tensor after applying a boolean_mask with XLA context will result in an error. The masked Tensor is not recognized as being smaller and can therefor not be reshaped into the correct size. 

In my particular case, working with the flattened tensor after applying the mask is not possible. A reshape into the right dimension is therefor necessary. Also applying the mask outside of the XLA context won't work in my context. I've also tried using tf.ragged.boolean_mask, however the error still persists and my further computations won't work with ragged Tensors.

### Standalone code to reproduce the issue

```shell
# Code is only for demonstration purpose and vastly simplified to show the error

A = tf.reshape(tf.range(1,10), [3,3])    #Creates an 3x3 Matrix with elements from 1-9
mask = tf.broadcast_to(tf.constant([True, False, True]), [3, 3])    #Creates mask with same dimensions as A

# Some functions for exploration of the error
# tf.boolean_mask may be substituted with tf.ragged.boolean_mask
@tf.function(jit_compile=True)
def func1(A, mask):
    return tf.boolean_mask(A, mask)

@tf.function(jit_compile=True)
def func2(A, mask):
    return tf.reshape(tf.boolean_mask(A, mask), [3,3])

@tf.function(jit_compile=True)
def func3(A, mask):
    return tf.reshape(tf.boolean_mask(A, mask), [3,2])

func1(A, mask)
# When using tf.boolean_mask --> Will return the correct values, however in a flatted tensor
# When using tf.ragged.boolean_mask --> Will return the correct values, in the correct shape, however as a ragged tensor

func2(A, mask)   # --> An error should be thrown since only 6 elements are in the Tensor after masking
# When using tf.boolean_mask or tf.ragged.boolean_mask --> Will return <tf.Tensor: shape=(3, 3), dtype=int32, numpy=
#  array([[1, 3, 4],
#         [6, 7, 9],
#         [1, 1, 1]], dtype=int32)>
# which are NOT the correct values

func3(A, mask)    # Should work
# When using tf.boolean_mask or tf.ragged.boolean_mask --> Will throw an error as provided below
```


### Relevant log output

```shell
W tensorflow/core/framework/op_kernel.cc:1839] OP_REQUIRES failed at xla_ops.cc:574 : INVALID_ARGUMENT: Input to reshape is a tensor with 9 values, but the requested shape has 6
```
