Forward AD threw error in tf.autodiff.ForwardAccumulator for tf.keras.layers.AveragePooling2D, but backward AD succeeded with same input
Hi @cheyennee ,

I have replicated the reported behaviour with TF2.14 and tf-nightly as well. Attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/6d5f658f11168b4c2a2581078dec3280/62422.ipynb) for reference.

Will dig more into for the root cause and come back. Thanks!
### Issue type

Bug

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

No

### Source

source

### TensorFlow version

tf 2.14.0

### Custom code

Yes

### OS platform and distribution

windows colab

### Mobile device

_No response_

### Python version

3.10

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

Forward AD threw error in tf.autodiff.ForwardAccumulator for tf.keras.layers.AveragePooling2D, but backward AD succeeded with same input

### Standalone code to reproduce the issue

```shell
pool_size_0 = 16
pool_size_1 = 3
pool_size = [pool_size_0,pool_size_1, ]
strides = None
padding = "valid"
data_format = None
__input___0_tensor = tf.random.uniform([2, 32, 1, 1], minval=0, maxval=2, dtype=tf.float32)
__input___0 = tf.identity(__input___0_tensor)
inputs = __input___0
layer = tf.keras.layers.AveragePooling2D(pool_size=pool_size, strides=strides, padding=padding, data_format=data_format,  dtype=tf.float64)

with tf.GradientTape(persistent=True, ) as g:
    g.watch(inputs)
    res_backward = layer(inputs)
grad_backward = g.jacobian(res_backward, res_backward)
print("res_backward:", res_backward)
print("grad_backward:", grad_backward)

tangents = tf.constant(1., dtype=tf.float32, shape=[2, 32, 1, 1])

with tf.autodiff.ForwardAccumulator(inputs, tangents) as acc:
    res_forward = layer(inputs)
    grad_jvp = acc.jvp(res_forward)
    print("res_forward:", res_forward)
    print("grad_forward", grad_jvp)
```


### Relevant log output

```shell
res_backward: tf.Tensor([], shape=(2, 2, 0, 1), dtype=float64)
grad_backward: tf.Tensor([], shape=(2, 2, 0, 1, 2, 2, 0, 1), dtype=float64)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-43-1e65b8d83cd7> in <cell line: 22>()
     21 
     22 with tf.autodiff.ForwardAccumulator(inputs, tangents) as acc:
---> 23     res_forward = layer(inputs)
     24     grad_jvp = acc.jvp(res_forward)
     25     print("res_forward:", res_forward)

1 frames
/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py in _create_c_op(graph, node_def, inputs, control_inputs, op_def, extract_traceback)
   1019   except errors.InvalidArgumentError as e:
   1020     # Convert to ValueError for backwards compatibility.
-> 1021     raise ValueError(e.message)
   1022 
   1023   # Record the current Python stack trace as the creating stacktrace of this

ValueError: Exception encountered when calling layer 'average_pooling2d_12' (type AveragePooling2D).

Negative dimension size caused by subtracting 3 from 1 for '{{node gradient_tape/gradient_tape/AvgPool}} = AvgPool[T=DT_DOUBLE, data_format="NHWC", ksize=[1, 16, 3, 1], padding="VALID", strides=[1, 16, 3, 1]](tangents)' with input shapes: [2,32,1,1].

Call arguments received by layer 'average_pooling2d_12' (type AveragePooling2D):
  • inputs=tf.Tensor(shape=(2, 32, 1, 1), dtype=float64)
```
