We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
CenterCrop.compute_output_shape() does not consider the list compatibility
When the input is a list of tensor, CenterCrop can correctly generate the output, while` CenterCrop.compute_output_shape()` will generate a wrong shape. Please refer to this [gist](https://colab.research.google.com/drive/1JLuEr0-dNCmawTb2dRX7kaSxOKGZ6Obv?usp=sharing).

Interestingly, I found the test code in [center_crop_test.py, ](https://github.com/keras-team/keras/blob/master/keras/layers/preprocessing/center_crop_test.py#L178) has tested the list compatibility, while its `compute_output_shape` function missed it.

--- 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/preprocessing/center_crop.py`
Content:
```
1 from keras import backend
2 from keras.api_export import keras_export
3 from keras.layers.preprocessing.tf_data_layer import TFDataLayer
4 from keras.utils import image_utils
5 
6 
7 @keras_export("keras.layers.CenterCrop")
8 class CenterCrop(TFDataLayer):
9     """A preprocessing layer which crops images.
10 
11     This layers crops the central portion of the images to a target size. If an
12     image is smaller than the target size, it will be resized and cropped
13     so as to return the largest possible window in the image that matches
14     the target aspect ratio.
15 
16     Input pixel values can be of any range (e.g. `[0., 1.)` or `[0, 255]`).
17 
18     Input shape:
19         3D (unbatched) or 4D (batched) tensor with shape:
20         `(..., height, width, channels)`, in `"channels_last"` format,
21         or `(..., channels, height, width)`, in `"channels_first"` format.
22 
23     Output shape:
24         3D (unbatched) or 4D (batched) tensor with shape:
25         `(..., target_height, target_width, channels)`,
26         or `(..., channels, target_height, target_width)`,
27         in `"channels_first"` format.
28 
29     If the input height/width is even and the target height/width is odd (or
30     inversely), the input image is left-padded by 1 pixel.
31 
32     **Note:** This layer is safe to use inside a `tf.data` pipeline
33     (independently of which backend you're using).
34 
35     Args:
36         height: Integer, the height of the output shape.
37         width: Integer, the width of the output shape.
38         data_format: string, either `"channels_last"` or `"channels_first"`.
39             The ordering of the dimensions in the inputs. `"channels_last"`
40             corresponds to inputs with shape `(batch, height, width, channels)`
41             while `"channels_first"` corresponds to inputs with shape
42             `(batch, channels, height, width)`. It defaults to the
43             `image_data_format` value found in your Keras config file at
44             `~/.keras/keras.json`. If you never set it, then it will be
45             `"channels_last"`.
46     """
47 
48     def __init__(self, height, width, data_format=None, **kwargs):
49         super().__init__(**kwargs)
50         self.height = height
51         self.width = width
52         self.data_format = backend.standardize_data_format(data_format)
53 
54     def call(self, inputs):
55         inputs = self.backend.cast(inputs, self.compute_dtype)
56         if self.data_format == "channels_first":
57             init_height = inputs.shape[-2]
58             init_width = inputs.shape[-1]
59         else:
60             init_height = inputs.shape[-3]
61             init_width = inputs.shape[-2]
62 
63         if init_height is None or init_width is None:
64             # Dynamic size case. TODO.
65             raise ValueError(
66                 "At this time, CenterCrop can only "
67                 "process images with a static spatial "
68                 f"shape. Received: inputs.shape={inputs.shape}"
69             )
70 
71         h_diff = init_height - self.height
72         w_diff = init_width - self.width
73 
74         h_start = int(h_diff / 2)
75         w_start = int(w_diff / 2)
76 
77         if h_diff >= 0 and w_diff >= 0:
78             if len(inputs.shape) == 4:
79                 if self.data_format == "channels_first":
80                     return inputs[
81                         :,
82                         :,
83                         h_start : h_start + self.height,
84                         w_start : w_start + self.width,
85                     ]
86                 return inputs[
87                     :,
88                     h_start : h_start + self.height,
89                     w_start : w_start + self.width,
90                     :,
91                 ]
92             elif len(inputs.shape) == 3:
93                 if self.data_format == "channels_first":
94                     return inputs[
95                         :,
96                         h_start : h_start + self.height,
97                         w_start : w_start + self.width,
98                     ]
99                 return inputs[
100                     h_start : h_start + self.height,
101                     w_start : w_start + self.width,
102                     :,
103                 ]
104 
105         return image_utils.smart_resize(
106             inputs,
107             [self.height, self.width],
108             data_format=self.data_format,
109             backend_module=self.backend,
110         )
111 
112     def compute_output_shape(self, input_shape):
113         input_shape = list(input_shape)
114         if len(input_shape) == 4:
115             if self.data_format == "channels_last":
116                 input_shape[1] = self.height
117                 input_shape[2] = self.width
118             else:
119                 input_shape[2] = self.height
120                 input_shape[3] = self.width
121         else:
122             if self.data_format == "channels_last":
123                 input_shape[0] = self.height
124                 input_shape[1] = self.width
125             else:
126                 input_shape[1] = self.height
127                 input_shape[2] = self.width
128         return tuple(input_shape)
129 
130     def get_config(self):
131         base_config = super().get_config()
132         config = {
133             "height": self.height,
134             "width": self.width,
135             "data_format": self.data_format,
136         }
137         return {**base_config, **config}
138
```
--- 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()

```

diff --git a/keras/layers/preprocessing/center_crop.py b/keras/layers/preprocessing/center_crop.py
--- a/keras/layers/preprocessing/center_crop.py
+++ b/keras/layers/preprocessing/center_crop.py
@@ -111,6 +111,13 @@
 
     def compute_output_shape(self, input_shape):
         input_shape = list(input_shape)
+        if isinstance(input_shape[0], (list, tuple)) or len(
+            input_shape
+        ) not in (3, 4):
+            raise ValueError(
+                "`input_shape` must be a non-nested tuple or list "
+                "of rank-1 with size 3 (unbatched) or 4 (batched). "
+            )
         if len(input_shape) == 4:
             if self.data_format == "channels_last":
                 input_shape[1] = self.height

{"golden_diff": "diff --git a/keras/layers/preprocessing/center_crop.py b/keras/layers/preprocessing/center_crop.py\n--- a/keras/layers/preprocessing/center_crop.py\n+++ b/keras/layers/preprocessing/center_crop.py\n@@ -111,6 +111,13 @@\n \n     def compute_output_shape(self, input_shape):\n         input_shape = list(input_shape)\n+        if isinstance(input_shape[0], (list, tuple)) or len(\n+            input_shape\n+        ) not in (3, 4):\n+            raise ValueError(\n+                \"`input_shape` must be a non-nested tuple or list \"\n+                \"of rank-1 with size 3 (unbatched) or 4 (batched). \"\n+            )\n         if len(input_shape) == 4:\n             if self.data_format == \"channels_last\":\n                 input_shape[1] = self.height\n", "issue": "CenterCrop.compute_output_shape() does not consider the list compatibility\nWhen the input is a list of tensor, CenterCrop can correctly generate the output, while` CenterCrop.compute_output_shape()` will generate a wrong shape. Please refer to this [gist](https://colab.research.google.com/drive/1JLuEr0-dNCmawTb2dRX7kaSxOKGZ6Obv?usp=sharing).\r\n\r\nInterestingly, I found the test code in [center_crop_test.py, ](https://github.com/keras-team/keras/blob/master/keras/layers/preprocessing/center_crop_test.py#L178) has tested the list compatibility, while its `compute_output_shape` function missed it.\n", "before_files": [{"content": "from keras import backend\nfrom keras.api_export import keras_export\nfrom keras.layers.preprocessing.tf_data_layer import TFDataLayer\nfrom keras.utils import image_utils\n\n\n@keras_export(\"keras.layers.CenterCrop\")\nclass CenterCrop(TFDataLayer):\n    \"\"\"A preprocessing layer which crops images.\n\n    This layers crops the central portion of the images to a target size. If an\n    image is smaller than the target size, it will be resized and cropped\n    so as to return the largest possible window in the image that matches\n    the target aspect ratio.\n\n    Input pixel values can be of any range (e.g. `[0., 1.)` or `[0, 255]`).\n\n    Input shape:\n        3D (unbatched) or 4D (batched) tensor with shape:\n        `(..., height, width, channels)`, in `\"channels_last\"` format,\n        or `(..., channels, height, width)`, in `\"channels_first\"` format.\n\n    Output shape:\n        3D (unbatched) or 4D (batched) tensor with shape:\n        `(..., target_height, target_width, channels)`,\n        or `(..., channels, target_height, target_width)`,\n        in `\"channels_first\"` format.\n\n    If the input height/width is even and the target height/width is odd (or\n    inversely), the input image is left-padded by 1 pixel.\n\n    **Note:** This layer is safe to use inside a `tf.data` pipeline\n    (independently of which backend you're using).\n\n    Args:\n        height: Integer, the height of the output shape.\n        width: Integer, the width of the output shape.\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 `(batch, height, width, channels)`\n            while `\"channels_first\"` corresponds to inputs with shape\n            `(batch, 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    \"\"\"\n\n    def __init__(self, height, width, data_format=None, **kwargs):\n        super().__init__(**kwargs)\n        self.height = height\n        self.width = width\n        self.data_format = backend.standardize_data_format(data_format)\n\n    def call(self, inputs):\n        inputs = self.backend.cast(inputs, self.compute_dtype)\n        if self.data_format == \"channels_first\":\n            init_height = inputs.shape[-2]\n            init_width = inputs.shape[-1]\n        else:\n            init_height = inputs.shape[-3]\n            init_width = inputs.shape[-2]\n\n        if init_height is None or init_width is None:\n            # Dynamic size case. TODO.\n            raise ValueError(\n                \"At this time, CenterCrop can only \"\n                \"process images with a static spatial \"\n                f\"shape. Received: inputs.shape={inputs.shape}\"\n            )\n\n        h_diff = init_height - self.height\n        w_diff = init_width - self.width\n\n        h_start = int(h_diff / 2)\n        w_start = int(w_diff / 2)\n\n        if h_diff >= 0 and w_diff >= 0:\n            if len(inputs.shape) == 4:\n                if self.data_format == \"channels_first\":\n                    return inputs[\n                        :,\n                        :,\n                        h_start : h_start + self.height,\n                        w_start : w_start + self.width,\n                    ]\n                return inputs[\n                    :,\n                    h_start : h_start + self.height,\n                    w_start : w_start + self.width,\n                    :,\n                ]\n            elif len(inputs.shape) == 3:\n                if self.data_format == \"channels_first\":\n                    return inputs[\n                        :,\n                        h_start : h_start + self.height,\n                        w_start : w_start + self.width,\n                    ]\n                return inputs[\n                    h_start : h_start + self.height,\n                    w_start : w_start + self.width,\n                    :,\n                ]\n\n        return image_utils.smart_resize(\n            inputs,\n            [self.height, self.width],\n            data_format=self.data_format,\n            backend_module=self.backend,\n        )\n\n    def compute_output_shape(self, input_shape):\n        input_shape = list(input_shape)\n        if len(input_shape) == 4:\n            if self.data_format == \"channels_last\":\n                input_shape[1] = self.height\n                input_shape[2] = self.width\n            else:\n                input_shape[2] = self.height\n                input_shape[3] = self.width\n        else:\n            if self.data_format == \"channels_last\":\n                input_shape[0] = self.height\n                input_shape[1] = self.width\n            else:\n                input_shape[1] = self.height\n                input_shape[2] = self.width\n        return tuple(input_shape)\n\n    def get_config(self):\n        base_config = super().get_config()\n        config = {\n            \"height\": self.height,\n            \"width\": self.width,\n            \"data_format\": self.data_format,\n        }\n        return {**base_config, **config}\n", "path": "keras/layers/preprocessing/center_crop.py"}], "after_files": [{"content": "from keras import backend\nfrom keras.api_export import keras_export\nfrom keras.layers.preprocessing.tf_data_layer import TFDataLayer\nfrom keras.utils import image_utils\n\n\n@keras_export(\"keras.layers.CenterCrop\")\nclass CenterCrop(TFDataLayer):\n    \"\"\"A preprocessing layer which crops images.\n\n    This layers crops the central portion of the images to a target size. If an\n    image is smaller than the target size, it will be resized and cropped\n    so as to return the largest possible window in the image that matches\n    the target aspect ratio.\n\n    Input pixel values can be of any range (e.g. `[0., 1.)` or `[0, 255]`).\n\n    Input shape:\n        3D (unbatched) or 4D (batched) tensor with shape:\n        `(..., height, width, channels)`, in `\"channels_last\"` format,\n        or `(..., channels, height, width)`, in `\"channels_first\"` format.\n\n    Output shape:\n        3D (unbatched) or 4D (batched) tensor with shape:\n        `(..., target_height, target_width, channels)`,\n        or `(..., channels, target_height, target_width)`,\n        in `\"channels_first\"` format.\n\n    If the input height/width is even and the target height/width is odd (or\n    inversely), the input image is left-padded by 1 pixel.\n\n    **Note:** This layer is safe to use inside a `tf.data` pipeline\n    (independently of which backend you're using).\n\n    Args:\n        height: Integer, the height of the output shape.\n        width: Integer, the width of the output shape.\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 `(batch, height, width, channels)`\n            while `\"channels_first\"` corresponds to inputs with shape\n            `(batch, 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    \"\"\"\n\n    def __init__(self, height, width, data_format=None, **kwargs):\n        super().__init__(**kwargs)\n        self.height = height\n        self.width = width\n        self.data_format = backend.standardize_data_format(data_format)\n\n    def call(self, inputs):\n        inputs = self.backend.cast(inputs, self.compute_dtype)\n        if self.data_format == \"channels_first\":\n            init_height = inputs.shape[-2]\n            init_width = inputs.shape[-1]\n        else:\n            init_height = inputs.shape[-3]\n            init_width = inputs.shape[-2]\n\n        if init_height is None or init_width is None:\n            # Dynamic size case. TODO.\n            raise ValueError(\n                \"At this time, CenterCrop can only \"\n                \"process images with a static spatial \"\n                f\"shape. Received: inputs.shape={inputs.shape}\"\n            )\n\n        h_diff = init_height - self.height\n        w_diff = init_width - self.width\n\n        h_start = int(h_diff / 2)\n        w_start = int(w_diff / 2)\n\n        if h_diff >= 0 and w_diff >= 0:\n            if len(inputs.shape) == 4:\n                if self.data_format == \"channels_first\":\n                    return inputs[\n                        :,\n                        :,\n                        h_start : h_start + self.height,\n                        w_start : w_start + self.width,\n                    ]\n                return inputs[\n                    :,\n                    h_start : h_start + self.height,\n                    w_start : w_start + self.width,\n                    :,\n                ]\n            elif len(inputs.shape) == 3:\n                if self.data_format == \"channels_first\":\n                    return inputs[\n                        :,\n                        h_start : h_start + self.height,\n                        w_start : w_start + self.width,\n                    ]\n                return inputs[\n                    h_start : h_start + self.height,\n                    w_start : w_start + self.width,\n                    :,\n                ]\n\n        return image_utils.smart_resize(\n            inputs,\n            [self.height, self.width],\n            data_format=self.data_format,\n            backend_module=self.backend,\n        )\n\n    def compute_output_shape(self, input_shape):\n        input_shape = list(input_shape)\n        if isinstance(input_shape[0], (list, tuple)) or len(\n            input_shape\n        ) not in (3, 4):\n            raise ValueError(\n                \"`input_shape` must be a non-nested tuple or list \"\n                \"of rank-1 with size 3 (unbatched) or 4 (batched). \"\n            )\n        if len(input_shape) == 4:\n            if self.data_format == \"channels_last\":\n                input_shape[1] = self.height\n                input_shape[2] = self.width\n            else:\n                input_shape[2] = self.height\n                input_shape[3] = self.width\n        else:\n            if self.data_format == \"channels_last\":\n                input_shape[0] = self.height\n                input_shape[1] = self.width\n            else:\n                input_shape[1] = self.height\n                input_shape[2] = self.width\n        return tuple(input_shape)\n\n    def get_config(self):\n        base_config = super().get_config()\n        config = {\n            \"height\": self.height,\n            \"width\": self.width,\n            \"data_format\": self.data_format,\n        }\n        return {**base_config, **config}\n", "path": "keras/layers/preprocessing/center_crop.py"}]}