We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Doc Description Wrong
Hello, 

I find python doc in [get_depth_image_from_path](https://github.com/nerfstudio-project/nerfstudio/blob/main/nerfstudio/data/utils/data_utils.py) is wrong about the return tensor shape, 

it should be [height, width, 1] not [width, height, 1].

![图片](https://github.com/nerfstudio-project/nerfstudio/assets/20349525/e6acb5ee-4f66-44ad-b21e-1161b1df61b3)

a simple verification:

![图片](https://github.com/nerfstudio-project/nerfstudio/assets/20349525/dfd65f57-7f8b-44ab-897b-d06793c03375)



--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `nerfstudio/data/utils/data_utils.py`
Content:
```
1 # Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
2 #
3 # Licensed under the Apache License, Version 2.0 (the "License");
4 # you may not use this file except in compliance with the License.
5 # You may obtain a copy of the License at
6 #
7 #     http://www.apache.org/licenses/LICENSE-2.0
8 #
9 # Unless required by applicable law or agreed to in writing, software
10 # distributed under the License is distributed on an "AS IS" BASIS,
11 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12 # See the License for the specific language governing permissions and
13 # limitations under the License.
14 
15 """Utility functions to allow easy re-use of common operations across dataloaders"""
16 from pathlib import Path
17 from typing import List, Tuple, Union
18 
19 import cv2
20 import numpy as np
21 import torch
22 from PIL import Image
23 
24 
25 def get_image_mask_tensor_from_path(filepath: Path, scale_factor: float = 1.0) -> torch.Tensor:
26     """
27     Utility function to read a mask image from the given path and return a boolean tensor
28     """
29     pil_mask = Image.open(filepath)
30     if scale_factor != 1.0:
31         width, height = pil_mask.size
32         newsize = (int(width * scale_factor), int(height * scale_factor))
33         pil_mask = pil_mask.resize(newsize, resample=Image.NEAREST)
34     mask_tensor = torch.from_numpy(np.array(pil_mask)).unsqueeze(-1).bool()
35     if len(mask_tensor.shape) != 3:
36         raise ValueError("The mask image should have 1 channel")
37     return mask_tensor
38 
39 
40 def get_semantics_and_mask_tensors_from_path(
41     filepath: Path, mask_indices: Union[List, torch.Tensor], scale_factor: float = 1.0
42 ) -> Tuple[torch.Tensor, torch.Tensor]:
43     """
44     Utility function to read segmentation from the given filepath
45     If no mask is required - use mask_indices = []
46     """
47     if isinstance(mask_indices, List):
48         mask_indices = torch.tensor(mask_indices, dtype=torch.int64).view(1, 1, -1)
49     pil_image = Image.open(filepath)
50     if scale_factor != 1.0:
51         width, height = pil_image.size
52         newsize = (int(width * scale_factor), int(height * scale_factor))
53         pil_image = pil_image.resize(newsize, resample=Image.NEAREST)
54     semantics = torch.from_numpy(np.array(pil_image, dtype="int64"))[..., None]
55     mask = torch.sum(semantics == mask_indices, dim=-1, keepdim=True) == 0
56     return semantics, mask
57 
58 
59 def get_depth_image_from_path(
60     filepath: Path,
61     height: int,
62     width: int,
63     scale_factor: float,
64     interpolation: int = cv2.INTER_NEAREST,
65 ) -> torch.Tensor:
66     """Loads, rescales and resizes depth images.
67     Filepath points to a 16-bit or 32-bit depth image, or a numpy array `*.npy`.
68 
69     Args:
70         filepath: Path to depth image.
71         height: Target depth image height.
72         width: Target depth image width.
73         scale_factor: Factor by which to scale depth image.
74         interpolation: Depth value interpolation for resizing.
75 
76     Returns:
77         Depth image torch tensor with shape [width, height, 1].
78     """
79     if filepath.suffix == ".npy":
80         image = np.load(filepath) * scale_factor
81         image = cv2.resize(image, (width, height), interpolation=interpolation)
82     else:
83         image = cv2.imread(str(filepath.absolute()), cv2.IMREAD_ANYDEPTH)
84         image = image.astype(np.float64) * scale_factor
85         image = cv2.resize(image, (width, height), interpolation=interpolation)
86     return torch.from_numpy(image[:, :, np.newaxis])
87
```
--- 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/nerfstudio/data/utils/data_utils.py b/nerfstudio/data/utils/data_utils.py
--- a/nerfstudio/data/utils/data_utils.py
+++ b/nerfstudio/data/utils/data_utils.py
@@ -74,7 +74,7 @@
         interpolation: Depth value interpolation for resizing.
 
     Returns:
-        Depth image torch tensor with shape [width, height, 1].
+        Depth image torch tensor with shape [height, width, 1].
     """
     if filepath.suffix == ".npy":
         image = np.load(filepath) * scale_factor

{"golden_diff": "diff --git a/nerfstudio/data/utils/data_utils.py b/nerfstudio/data/utils/data_utils.py\n--- a/nerfstudio/data/utils/data_utils.py\n+++ b/nerfstudio/data/utils/data_utils.py\n@@ -74,7 +74,7 @@\n         interpolation: Depth value interpolation for resizing.\n \n     Returns:\n-        Depth image torch tensor with shape [width, height, 1].\n+        Depth image torch tensor with shape [height, width, 1].\n     \"\"\"\n     if filepath.suffix == \".npy\":\n         image = np.load(filepath) * scale_factor\n", "issue": "Doc Description Wrong\nHello, \r\n\r\nI find python doc in [get_depth_image_from_path](https://github.com/nerfstudio-project/nerfstudio/blob/main/nerfstudio/data/utils/data_utils.py) is wrong about the return tensor shape, \r\n\r\nit should be [height, width, 1] not [width, height, 1].\r\n\r\n![\u56fe\u7247](https://github.com/nerfstudio-project/nerfstudio/assets/20349525/e6acb5ee-4f66-44ad-b21e-1161b1df61b3)\r\n\r\na simple verification:\r\n\r\n![\u56fe\u7247](https://github.com/nerfstudio-project/nerfstudio/assets/20349525/dfd65f57-7f8b-44ab-897b-d06793c03375)\r\n\r\n\n", "before_files": [{"content": "# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"Utility functions to allow easy re-use of common operations across dataloaders\"\"\"\nfrom pathlib import Path\nfrom typing import List, Tuple, Union\n\nimport cv2\nimport numpy as np\nimport torch\nfrom PIL import Image\n\n\ndef get_image_mask_tensor_from_path(filepath: Path, scale_factor: float = 1.0) -> torch.Tensor:\n    \"\"\"\n    Utility function to read a mask image from the given path and return a boolean tensor\n    \"\"\"\n    pil_mask = Image.open(filepath)\n    if scale_factor != 1.0:\n        width, height = pil_mask.size\n        newsize = (int(width * scale_factor), int(height * scale_factor))\n        pil_mask = pil_mask.resize(newsize, resample=Image.NEAREST)\n    mask_tensor = torch.from_numpy(np.array(pil_mask)).unsqueeze(-1).bool()\n    if len(mask_tensor.shape) != 3:\n        raise ValueError(\"The mask image should have 1 channel\")\n    return mask_tensor\n\n\ndef get_semantics_and_mask_tensors_from_path(\n    filepath: Path, mask_indices: Union[List, torch.Tensor], scale_factor: float = 1.0\n) -> Tuple[torch.Tensor, torch.Tensor]:\n    \"\"\"\n    Utility function to read segmentation from the given filepath\n    If no mask is required - use mask_indices = []\n    \"\"\"\n    if isinstance(mask_indices, List):\n        mask_indices = torch.tensor(mask_indices, dtype=torch.int64).view(1, 1, -1)\n    pil_image = Image.open(filepath)\n    if scale_factor != 1.0:\n        width, height = pil_image.size\n        newsize = (int(width * scale_factor), int(height * scale_factor))\n        pil_image = pil_image.resize(newsize, resample=Image.NEAREST)\n    semantics = torch.from_numpy(np.array(pil_image, dtype=\"int64\"))[..., None]\n    mask = torch.sum(semantics == mask_indices, dim=-1, keepdim=True) == 0\n    return semantics, mask\n\n\ndef get_depth_image_from_path(\n    filepath: Path,\n    height: int,\n    width: int,\n    scale_factor: float,\n    interpolation: int = cv2.INTER_NEAREST,\n) -> torch.Tensor:\n    \"\"\"Loads, rescales and resizes depth images.\n    Filepath points to a 16-bit or 32-bit depth image, or a numpy array `*.npy`.\n\n    Args:\n        filepath: Path to depth image.\n        height: Target depth image height.\n        width: Target depth image width.\n        scale_factor: Factor by which to scale depth image.\n        interpolation: Depth value interpolation for resizing.\n\n    Returns:\n        Depth image torch tensor with shape [width, height, 1].\n    \"\"\"\n    if filepath.suffix == \".npy\":\n        image = np.load(filepath) * scale_factor\n        image = cv2.resize(image, (width, height), interpolation=interpolation)\n    else:\n        image = cv2.imread(str(filepath.absolute()), cv2.IMREAD_ANYDEPTH)\n        image = image.astype(np.float64) * scale_factor\n        image = cv2.resize(image, (width, height), interpolation=interpolation)\n    return torch.from_numpy(image[:, :, np.newaxis])\n", "path": "nerfstudio/data/utils/data_utils.py"}], "after_files": [{"content": "# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"Utility functions to allow easy re-use of common operations across dataloaders\"\"\"\nfrom pathlib import Path\nfrom typing import List, Tuple, Union\n\nimport cv2\nimport numpy as np\nimport torch\nfrom PIL import Image\n\n\ndef get_image_mask_tensor_from_path(filepath: Path, scale_factor: float = 1.0) -> torch.Tensor:\n    \"\"\"\n    Utility function to read a mask image from the given path and return a boolean tensor\n    \"\"\"\n    pil_mask = Image.open(filepath)\n    if scale_factor != 1.0:\n        width, height = pil_mask.size\n        newsize = (int(width * scale_factor), int(height * scale_factor))\n        pil_mask = pil_mask.resize(newsize, resample=Image.NEAREST)\n    mask_tensor = torch.from_numpy(np.array(pil_mask)).unsqueeze(-1).bool()\n    if len(mask_tensor.shape) != 3:\n        raise ValueError(\"The mask image should have 1 channel\")\n    return mask_tensor\n\n\ndef get_semantics_and_mask_tensors_from_path(\n    filepath: Path, mask_indices: Union[List, torch.Tensor], scale_factor: float = 1.0\n) -> Tuple[torch.Tensor, torch.Tensor]:\n    \"\"\"\n    Utility function to read segmentation from the given filepath\n    If no mask is required - use mask_indices = []\n    \"\"\"\n    if isinstance(mask_indices, List):\n        mask_indices = torch.tensor(mask_indices, dtype=torch.int64).view(1, 1, -1)\n    pil_image = Image.open(filepath)\n    if scale_factor != 1.0:\n        width, height = pil_image.size\n        newsize = (int(width * scale_factor), int(height * scale_factor))\n        pil_image = pil_image.resize(newsize, resample=Image.NEAREST)\n    semantics = torch.from_numpy(np.array(pil_image, dtype=\"int64\"))[..., None]\n    mask = torch.sum(semantics == mask_indices, dim=-1, keepdim=True) == 0\n    return semantics, mask\n\n\ndef get_depth_image_from_path(\n    filepath: Path,\n    height: int,\n    width: int,\n    scale_factor: float,\n    interpolation: int = cv2.INTER_NEAREST,\n) -> torch.Tensor:\n    \"\"\"Loads, rescales and resizes depth images.\n    Filepath points to a 16-bit or 32-bit depth image, or a numpy array `*.npy`.\n\n    Args:\n        filepath: Path to depth image.\n        height: Target depth image height.\n        width: Target depth image width.\n        scale_factor: Factor by which to scale depth image.\n        interpolation: Depth value interpolation for resizing.\n\n    Returns:\n        Depth image torch tensor with shape [height, width, 1].\n    \"\"\"\n    if filepath.suffix == \".npy\":\n        image = np.load(filepath) * scale_factor\n        image = cv2.resize(image, (width, height), interpolation=interpolation)\n    else:\n        image = cv2.imread(str(filepath.absolute()), cv2.IMREAD_ANYDEPTH)\n        image = image.astype(np.float64) * scale_factor\n        image = cv2.resize(image, (width, height), interpolation=interpolation)\n    return torch.from_numpy(image[:, :, np.newaxis])\n", "path": "nerfstudio/data/utils/data_utils.py"}]}