prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  Error in Documentation
  The documentation confuses the ‘channels_last’ input format. It needs to be changed to match the correct format ==> (batch_size, height, width, channels)
  
  https://github.com/keras-team/keras/blob/037ec9f5fc61a53c6e1f4c02b7bf1443429dcd45/keras/layers/convolutional/conv2d_transpose.py#L35
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `keras/layers/convolutional/conv2d_transpose.py`
  Content:
  ```
  1 from keras.api_export import keras_export
  2 from keras.layers.convolutional.base_conv_transpose import BaseConvTranspose
  3 
  4 
  5 @keras_export(
  6     [
  7         "keras.layers.Conv2DTranspose",
  8         "keras.layers.Convolution2DTranspose",
  9     ]
  10 )
  11 class Conv2DTranspose(BaseConvTranspose):
  12     """2D transposed convolution layer.
  13 
  14     The need for transposed convolutions generally arise from the desire to use
  15     a transformation going in the opposite direction of a normal convolution,
  16     i.e., from something that has the shape of the output of some convolution
  17     to something that has the shape of its input while maintaining a
  18     connectivity pattern that is compatible with said convolution.
  19 
  20     Args:
  21         filters: int, the dimension of the output space (the number of filters
  22             in the transposed convolution).
  23         kernel_size: int or tuple/list of 1 integer, specifying the size of the
  24             transposed convolution window.
  25         strides: int or tuple/list of 1 integer, specifying the stride length
  26             of the transposed convolution. `strides > 1` is incompatible with
  27             `dilation_rate > 1`.
  28         padding: string, either `"valid"` or `"same"` (case-insensitive).
  29             `"valid"` means no padding. `"same"` results in padding evenly to
  30             the left/right or up/down of the input. When `padding="same"` and
  31             `strides=1`, the output has the same size as the input.
  32         data_format: string, either `"channels_last"` or `"channels_first"`.
  33             The ordering of the dimensions in the inputs. `"channels_last"`
  34             corresponds to inputs with shape
  35             `(batch_size, channels, height, width)`
  36             while `"channels_first"` corresponds to inputs with shape
  37             `(batch_size, channels, height, width)`. It defaults to the
  38             `image_data_format` value found in your Keras config file at
  39             `~/.keras/keras.json`. If you never set it, then it will be
  40             `"channels_last"`.
  41         dilation_rate: int or tuple/list of 1 integers, specifying the dilation
  42             rate to use for dilated transposed convolution.
  43         activation: Activation function. If `None`, no activation is applied.
  44         use_bias: bool, if `True`, bias will be added to the output.
  45         kernel_initializer: Initializer for the convolution kernel. If `None`,
  46             the default initializer (`"glorot_uniform"`) will be used.
  47         bias_initializer: Initializer for the bias vector. If `None`, the
  48             default initializer (`"zeros"`) will be used.
  49         kernel_regularizer: Optional regularizer for the convolution kernel.
  50         bias_regularizer: Optional regularizer for the bias vector.
  51         activity_regularizer: Optional regularizer function for the output.
  52         kernel_constraint: Optional projection function to be applied to the
  53             kernel after being updated by an `Optimizer` (e.g. used to implement
  54             norm constraints or value constraints for layer weights). The
  55             function must take as input the unprojected variable and must return
  56             the projected variable (which must have the same shape). Constraints
  57             are not safe to use when doing asynchronous distributed training.
  58         bias_constraint: Optional projection function to be applied to the
  59             bias after being updated by an `Optimizer`.
  60 
  61     Input shape:
  62     - If `data_format="channels_last"`:
  63         A 4D tensor with shape: `(batch_size, height, width, channels)`
  64     - If `data_format="channels_first"`:
  65         A 4D tensor with shape: `(batch_size, channels, height, width)`
  66 
  67     Output shape:
  68     - If `data_format="channels_last"`:
  69         A 4D tensor with shape: `(batch_size, new_height, new_width, filters)`
  70     - If `data_format="channels_first"`:
  71         A 4D tensor with shape: `(batch_size, filters, new_height, new_width)`
  72 
  73     Returns:
  74         A 4D tensor representing
  75         `activation(conv2d_transpose(inputs, kernel) + bias)`.
  76 
  77     Raises:
  78         ValueError: when both `strides > 1` and `dilation_rate > 1`.
  79 
  80     References:
  81     - [A guide to convolution arithmetic for deep learning](
  82         https://arxiv.org/abs/1603.07285v1)
  83     - [Deconvolutional Networks](
  84         https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf)
  85 
  86     Examples:
  87 
  88     >>> x = np.random.rand(4, 10, 8, 128)
  89     >>> y = keras.layers.Conv2DTranspose(32, 2, 2, activation='relu')(x)
  90     >>> print(y.shape)
  91     (4, 20, 16, 32)
  92     """
  93 
  94     def __init__(
  95         self,
  96         filters,
  97         kernel_size,
  98         strides=(1, 1),
  99         padding="valid",
  100         data_format=None,
  101         dilation_rate=(1, 1),
  102         activation=None,
  103         use_bias=True,
  104         kernel_initializer="glorot_uniform",
  105         bias_initializer="zeros",
  106         kernel_regularizer=None,
  107         bias_regularizer=None,
  108         activity_regularizer=None,
  109         kernel_constraint=None,
  110         bias_constraint=None,
  111         **kwargs
  112     ):
  113         super().__init__(
  114             rank=2,
  115             filters=filters,
  116             kernel_size=kernel_size,
  117             strides=strides,
  118             padding=padding,
  119             data_format=data_format,
  120             dilation_rate=dilation_rate,
  121             activation=activation,
  122             use_bias=use_bias,
  123             kernel_initializer=kernel_initializer,
  124             bias_initializer=bias_initializer,
  125             kernel_regularizer=kernel_regularizer,
  126             bias_regularizer=bias_regularizer,
  127             activity_regularizer=activity_regularizer,
  128             kernel_constraint=kernel_constraint,
  129             bias_constraint=bias_constraint,
  130             **kwargs
  131         )
  132
  ```
  --- END FILES ---
  
  Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.
  
  Here is an example:
  
  ```diff
  diff --git a/examples/server_async.py b/examples/server_async.py
  --- a/examples/server_async.py
  +++ b/examples/server_async.py
  @@ -313,4 +313,4 @@
   
   
   if __name__ == "__main__":
  -    asyncio.run(run_async_server("."), debug=True)
  +    asyncio.run(run_async_server(), debug=True)
  diff --git a/examples/server_sync.py b/examples/server_sync.py
  --- a/examples/server_sync.py
  +++ b/examples/server_sync.py
  @@ -313,5 +313,5 @@
   
   
   if __name__ == "__main__":
  -    server = run_sync_server(".")
  +    server = run_sync_server()
       server.shutdown()
  
  ```
  
golden_diff: diff --git a/keras/layers/convolutional/conv2d_transpose.py b/keras/layers/convolutional/conv2d_transpose.py
  --- a/keras/layers/convolutional/conv2d_transpose.py
  +++ b/keras/layers/convolutional/conv2d_transpose.py
  @@ -32,7 +32,7 @@
           data_format: string, either `"channels_last"` or `"channels_first"`.
               The ordering of the dimensions in the inputs. `"channels_last"`
               corresponds to inputs with shape
  -            `(batch_size, channels, height, width)`
  +            `(batch_size, height, width, channels)`
               while `"channels_first"` corresponds to inputs with shape
               `(batch_size, channels, height, width)`. It defaults to the
               `image_data_format` value found in your Keras config file at
  
verification_info: {"golden_diff": "diff --git a/keras/layers/convolutional/conv2d_transpose.py b/keras/layers/convolutional/conv2d_transpose.py\n--- a/keras/layers/convolutional/conv2d_transpose.py\n+++ b/keras/layers/convolutional/conv2d_transpose.py\n@@ -32,7 +32,7 @@\n         data_format: string, either `\"channels_last\"` or `\"channels_first\"`.\n             The ordering of the dimensions in the inputs. `\"channels_last\"`\n             corresponds to inputs with shape\n-            `(batch_size, channels, height, width)`\n+            `(batch_size, height, width, channels)`\n             while `\"channels_first\"` corresponds to inputs with shape\n             `(batch_size, channels, height, width)`. It defaults to the\n             `image_data_format` value found in your Keras config file at\n", "issue": "Error in Documentation\nThe documentation confuses the \u2018channels_last\u2019 input format. It needs to be changed to match the correct format ==> (batch_size, height, width, channels)\r\n\r\nhttps://github.com/keras-team/keras/blob/037ec9f5fc61a53c6e1f4c02b7bf1443429dcd45/keras/layers/convolutional/conv2d_transpose.py#L35\n", "before_files": [{"content": "from keras.api_export import keras_export\nfrom keras.layers.convolutional.base_conv_transpose import BaseConvTranspose\n\n\n@keras_export(\n    [\n        \"keras.layers.Conv2DTranspose\",\n        \"keras.layers.Convolution2DTranspose\",\n    ]\n)\nclass Conv2DTranspose(BaseConvTranspose):\n    \"\"\"2D transposed convolution layer.\n\n    The need for transposed convolutions generally arise from the desire to use\n    a transformation going in the opposite direction of a normal convolution,\n    i.e., from something that has the shape of the output of some convolution\n    to something that has the shape of its input while maintaining a\n    connectivity pattern that is compatible with said convolution.\n\n    Args:\n        filters: int, the dimension of the output space (the number of filters\n            in the transposed convolution).\n        kernel_size: int or tuple/list of 1 integer, specifying the size of the\n            transposed convolution window.\n        strides: int or tuple/list of 1 integer, specifying the stride length\n            of the transposed convolution. `strides > 1` is incompatible with\n            `dilation_rate > 1`.\n        padding: string, either `\"valid\"` or `\"same\"` (case-insensitive).\n            `\"valid\"` means no padding. `\"same\"` results in padding evenly to\n            the left/right or up/down of the input. When `padding=\"same\"` and\n            `strides=1`, the output has the same size as the input.\n        data_format: string, either `\"channels_last\"` or `\"channels_first\"`.\n            The ordering of the dimensions in the inputs. `\"channels_last\"`\n            corresponds to inputs with shape\n            `(batch_size, channels, height, width)`\n            while `\"channels_first\"` corresponds to inputs with shape\n            `(batch_size, channels, height, width)`. It defaults to the\n            `image_data_format` value found in your Keras config file at\n            `~/.keras/keras.json`. If you never set it, then it will be\n            `\"channels_last\"`.\n        dilation_rate: int or tuple/list of 1 integers, specifying the dilation\n            rate to use for dilated transposed convolution.\n        activation: Activation function. If `None`, no activation is applied.\n        use_bias: bool, if `True`, bias will be added to the output.\n        kernel_initializer: Initializer for the convolution kernel. If `None`,\n            the default initializer (`\"glorot_uniform\"`) will be used.\n        bias_initializer: Initializer for the bias vector. If `None`, the\n            default initializer (`\"zeros\"`) will be used.\n        kernel_regularizer: Optional regularizer for the convolution kernel.\n        bias_regularizer: Optional regularizer for the bias vector.\n        activity_regularizer: Optional regularizer function for the output.\n        kernel_constraint: Optional projection function to be applied to the\n            kernel after being updated by an `Optimizer` (e.g. used to implement\n            norm constraints or value constraints for layer weights). The\n            function must take as input the unprojected variable and must return\n            the projected variable (which must have the same shape). Constraints\n            are not safe to use when doing asynchronous distributed training.\n        bias_constraint: Optional projection function to be applied to the\n            bias after being updated by an `Optimizer`.\n\n    Input shape:\n    - If `data_format=\"channels_last\"`:\n        A 4D tensor with shape: `(batch_size, height, width, channels)`\n    - If `data_format=\"channels_first\"`:\n        A 4D tensor with shape: `(batch_size, channels, height, width)`\n\n    Output shape:\n    - If `data_format=\"channels_last\"`:\n        A 4D tensor with shape: `(batch_size, new_height, new_width, filters)`\n    - If `data_format=\"channels_first\"`:\n        A 4D tensor with shape: `(batch_size, filters, new_height, new_width)`\n\n    Returns:\n        A 4D tensor representing\n        `activation(conv2d_transpose(inputs, kernel) + bias)`.\n\n    Raises:\n        ValueError: when both `strides > 1` and `dilation_rate > 1`.\n\n    References:\n    - [A guide to convolution arithmetic for deep learning](\n        https://arxiv.org/abs/1603.07285v1)\n    - [Deconvolutional Networks](\n        https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf)\n\n    Examples:\n\n    >>> x = np.random.rand(4, 10, 8, 128)\n    >>> y = keras.layers.Conv2DTranspose(32, 2, 2, activation='relu')(x)\n    >>> print(y.shape)\n    (4, 20, 16, 32)\n    \"\"\"\n\n    def __init__(\n        self,\n        filters,\n        kernel_size,\n        strides=(1, 1),\n        padding=\"valid\",\n        data_format=None,\n        dilation_rate=(1, 1),\n        activation=None,\n        use_bias=True,\n        kernel_initializer=\"glorot_uniform\",\n        bias_initializer=\"zeros\",\n        kernel_regularizer=None,\n        bias_regularizer=None,\n        activity_regularizer=None,\n        kernel_constraint=None,\n        bias_constraint=None,\n        **kwargs\n    ):\n        super().__init__(\n            rank=2,\n            filters=filters,\n            kernel_size=kernel_size,\n            strides=strides,\n            padding=padding,\n            data_format=data_format,\n            dilation_rate=dilation_rate,\n            activation=activation,\n            use_bias=use_bias,\n            kernel_initializer=kernel_initializer,\n            bias_initializer=bias_initializer,\n            kernel_regularizer=kernel_regularizer,\n            bias_regularizer=bias_regularizer,\n            activity_regularizer=activity_regularizer,\n            kernel_constraint=kernel_constraint,\n            bias_constraint=bias_constraint,\n            **kwargs\n        )\n", "path": "keras/layers/convolutional/conv2d_transpose.py"}], "after_files": [{"content": "from keras.api_export import keras_export\nfrom keras.layers.convolutional.base_conv_transpose import BaseConvTranspose\n\n\n@keras_export(\n    [\n        \"keras.layers.Conv2DTranspose\",\n        \"keras.layers.Convolution2DTranspose\",\n    ]\n)\nclass Conv2DTranspose(BaseConvTranspose):\n    \"\"\"2D transposed convolution layer.\n\n    The need for transposed convolutions generally arise from the desire to use\n    a transformation going in the opposite direction of a normal convolution,\n    i.e., from something that has the shape of the output of some convolution\n    to something that has the shape of its input while maintaining a\n    connectivity pattern that is compatible with said convolution.\n\n    Args:\n        filters: int, the dimension of the output space (the number of filters\n            in the transposed convolution).\n        kernel_size: int or tuple/list of 1 integer, specifying the size of the\n            transposed convolution window.\n        strides: int or tuple/list of 1 integer, specifying the stride length\n            of the transposed convolution. `strides > 1` is incompatible with\n            `dilation_rate > 1`.\n        padding: string, either `\"valid\"` or `\"same\"` (case-insensitive).\n            `\"valid\"` means no padding. `\"same\"` results in padding evenly to\n            the left/right or up/down of the input. When `padding=\"same\"` and\n            `strides=1`, the output has the same size as the input.\n        data_format: string, either `\"channels_last\"` or `\"channels_first\"`.\n            The ordering of the dimensions in the inputs. `\"channels_last\"`\n            corresponds to inputs with shape\n            `(batch_size, height, width, channels)`\n            while `\"channels_first\"` corresponds to inputs with shape\n            `(batch_size, channels, height, width)`. It defaults to the\n            `image_data_format` value found in your Keras config file at\n            `~/.keras/keras.json`. If you never set it, then it will be\n            `\"channels_last\"`.\n        dilation_rate: int or tuple/list of 1 integers, specifying the dilation\n            rate to use for dilated transposed convolution.\n        activation: Activation function. If `None`, no activation is applied.\n        use_bias: bool, if `True`, bias will be added to the output.\n        kernel_initializer: Initializer for the convolution kernel. If `None`,\n            the default initializer (`\"glorot_uniform\"`) will be used.\n        bias_initializer: Initializer for the bias vector. If `None`, the\n            default initializer (`\"zeros\"`) will be used.\n        kernel_regularizer: Optional regularizer for the convolution kernel.\n        bias_regularizer: Optional regularizer for the bias vector.\n        activity_regularizer: Optional regularizer function for the output.\n        kernel_constraint: Optional projection function to be applied to the\n            kernel after being updated by an `Optimizer` (e.g. used to implement\n            norm constraints or value constraints for layer weights). The\n            function must take as input the unprojected variable and must return\n            the projected variable (which must have the same shape). Constraints\n            are not safe to use when doing asynchronous distributed training.\n        bias_constraint: Optional projection function to be applied to the\n            bias after being updated by an `Optimizer`.\n\n    Input shape:\n    - If `data_format=\"channels_last\"`:\n        A 4D tensor with shape: `(batch_size, height, width, channels)`\n    - If `data_format=\"channels_first\"`:\n        A 4D tensor with shape: `(batch_size, channels, height, width)`\n\n    Output shape:\n    - If `data_format=\"channels_last\"`:\n        A 4D tensor with shape: `(batch_size, new_height, new_width, filters)`\n    - If `data_format=\"channels_first\"`:\n        A 4D tensor with shape: `(batch_size, filters, new_height, new_width)`\n\n    Returns:\n        A 4D tensor representing\n        `activation(conv2d_transpose(inputs, kernel) + bias)`.\n\n    Raises:\n        ValueError: when both `strides > 1` and `dilation_rate > 1`.\n\n    References:\n    - [A guide to convolution arithmetic for deep learning](\n        https://arxiv.org/abs/1603.07285v1)\n    - [Deconvolutional Networks](\n        https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf)\n\n    Examples:\n\n    >>> x = np.random.rand(4, 10, 8, 128)\n    >>> y = keras.layers.Conv2DTranspose(32, 2, 2, activation='relu')(x)\n    >>> print(y.shape)\n    (4, 20, 16, 32)\n    \"\"\"\n\n    def __init__(\n        self,\n        filters,\n        kernel_size,\n        strides=(1, 1),\n        padding=\"valid\",\n        data_format=None,\n        dilation_rate=(1, 1),\n        activation=None,\n        use_bias=True,\n        kernel_initializer=\"glorot_uniform\",\n        bias_initializer=\"zeros\",\n        kernel_regularizer=None,\n        bias_regularizer=None,\n        activity_regularizer=None,\n        kernel_constraint=None,\n        bias_constraint=None,\n        **kwargs\n    ):\n        super().__init__(\n            rank=2,\n            filters=filters,\n            kernel_size=kernel_size,\n            strides=strides,\n            padding=padding,\n            data_format=data_format,\n            dilation_rate=dilation_rate,\n            activation=activation,\n            use_bias=use_bias,\n            kernel_initializer=kernel_initializer,\n            bias_initializer=bias_initializer,\n            kernel_regularizer=kernel_regularizer,\n            bias_regularizer=bias_regularizer,\n            activity_regularizer=activity_regularizer,\n            kernel_constraint=kernel_constraint,\n            bias_constraint=bias_constraint,\n            **kwargs\n        )\n", "path": "keras/layers/convolutional/conv2d_transpose.py"}]}