Bug in backward gradient for tf.keras.layers.Conv2DTranspose
close as it fixed in tf v2.14, while can be repro in tf v2.9  Are you satisfied with the resolution of your issue?
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### 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

_No response_

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

The shape of res_backward is (1, 5, 6, 2), the shape of res_forward is (1, 5, 6, 2), the shape of grad_jvp is (1, 5, 6, 2), while the shape of backward gradient is (1, 5, 6, 2, 1, 5, 6, 2). I think both the values and shapes of grad_backward and grad_jvp should be the same. 

### Standalone code to reproduce the issue

```shell
filters = 2
kernel_size_0 = 3
kernel_size_1 = 3
kernel_size = [kernel_size_0,kernel_size_1, ]
strides_0 = 1
strides_1 = 1
strides = [
    strides_0,
    strides_1, ]
padding = "same"
output_padding = None
data_format = "channels_last"
dilation_rate_0 = 2
dilation_rate_1 = 2
dilation_rate = [dilation_rate_0,dilation_rate_1,]
activation = "linear"
use_bias = True
kernel_initializer = None
bias_initializer = None
kernel_regularizer = None
bias_regularizer = None
activity_regularizer = None
kernel_constraint = None
bias_constraint = None
__input___0_tensor = tf.random.uniform([1, 5, 6, 1], minval=-2, maxval=2, dtype=tf.float32)
__input___0 = tf.identity(__input___0_tensor)
Conv2DTranspose_class = tf.keras.layers.Conv2DTranspose(filters, kernel_size, strides=strides, padding=padding, output_padding=output_padding, data_format=data_format, dilation_rate=dilation_rate, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint,  dtype=tf.float32)
layer = Conv2DTranspose_class
inputs = __input___0

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=[1, 5, 6, 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

_No response_