The documentation for Conv1DTranspose does not state that the CPU does not support dilation rates larger than 1
Hi **@PhyllisJi** ,
- Sorry for the dealy, Please post this issue on [keras-team/keras repo.](https://github.com/keras-team/keras/issues)
To know more see;
[https://discuss.tensorflow.org/t/keras-project-moved-to-new-repository-in-https-github-com-keras-team-keras/1999](https://discuss.tensorflow.org/t/keras-project-moved-to-new-repository-in-https-github-com-keras-team-keras/1999).

Thank you!
### Issue type

Documentation Bug

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

Yes

### Source

source

### TensorFlow version

tf 2.14.0

### Custom code

Yes

### OS platform and distribution

Ubuntu 20.04

### 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?

Throw Error:
```
tensorflow.python.framework.errors_impl.InvalidArgumentError: Exception encountered when calling layer 'conv4_mutated' (type Conv1DTranspose).

{{function_node __wrapped__Conv2DBackpropInput_device_/job:localhost/replica:0/task:0/device:CPU:0}} Current CPU implementations do not yet support dilation rates larger than 1. [Op:Conv2DBackpropInput] name: 

Call arguments received by layer 'conv4_mutated' (type Conv1DTranspose):
  • inputs=tf.Tensor(shape=(2, 2048, 64), dtype=float32)
```
Also, I'm not quite sure why the error message relates to Conv2DBackpropInput

### Standalone code to reproduce the issue

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


os.environ['CUDA_VISIBLE_DEVICES'] = ''

def Model_HqTi1yjRFc7Dz1RLSJOvA9XX16R5Wp01(x):
    x = tf.keras.Input(shape=x)
    _x = x
    _zeropadding_x = tf.keras.layers.ZeroPadding1D(padding=(0, 0))(x)
    x = tf.keras.layers.Conv1D(filters=64, kernel_size=1, strides=1, padding="valid", data_format="channels_last", dilation_rate=1, groups=1, use_bias=True, name="conv1")(_zeropadding_x)
    x = tf.keras.layers.BatchNormalization(axis=-1, epsilon=1e-05, momentum=0.9, center=True, scale=True, name="bn1")(x)
    x = tf.nn.relu(x)
    _zeropadding_x = tf.keras.layers.ZeroPadding1D(padding=(0, 0))(x)
    x = tf.keras.layers.Conv1D(filters=64, kernel_size=1, strides=1, padding="valid", data_format="channels_last", dilation_rate=1, groups=1, use_bias=True, name="conv2")(_zeropadding_x)
    x = tf.keras.layers.BatchNormalization(axis=-1, epsilon=1e-05, momentum=0.9, center=True, scale=True, name="bn2")(x)
    x = tf.nn.relu(x)
    _zeropadding_x = tf.keras.layers.ZeroPadding1D(padding=(0, 0))(x)
    x = tf.keras.layers.Conv1D(filters=64, kernel_size=1, strides=1, padding="valid", data_format="channels_last", dilation_rate=1, groups=1, use_bias=True, name="conv3")(_zeropadding_x)
    x = tf.keras.layers.BatchNormalization(axis=-1, epsilon=1e-05, momentum=0.9, center=True, scale=True, name="bn3")(x)
    x = tf.keras.activations.relu(x)
    _zeropadding_x = tf.keras.layers.ZeroPadding1D(padding=(0, 0))(x)
    x = tf.keras.layers.Conv1DTranspose(filters=128, kernel_size=1, strides=1, padding="valid", output_padding=0, data_format="channels_last", dilation_rate=8, use_bias=True, name="conv4_mutated")(x)

    x = x
    model = tf.keras.models.Model(inputs=_x, outputs=x)
    return model


def go():
    with tf.device('/CPU:0'):
        shape = [2, 3, 2048]
        _numpy = np.random.random(shape).astype(np.float32)
        tf_input = tf.convert_to_tensor(_numpy.transpose(0, 2, 1), dtype=tf.float32)
        tf_model = Model_HqTi1yjRFc7Dz1RLSJOvA9XX16R5Wp01(tf_input.shape[1:])
        tf_output = tf_model(tf_input)




go()
```


### Relevant log output

_No response_