We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
[Docs] Missing Grid.py documentation
# 📚 Documentation/Examples

** Is there documentation missing? **
The utils section of [GPyTorch documentation](https://gpytorch.readthedocs.io) does not include any information on grid.py, which is referenced [elsewhere in the docs](https://docs.gpytorch.ai/en/stable/kernels.html?highlight=choose_grid_size#gpytorch.kernels.GridKernel.update_grid).


--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `gpytorch/utils/grid.py`
Content:
```
1 #!/usr/bin/env python3
2 
3 import math
4 from typing import List, Tuple
5 
6 import torch
7 
8 
9 def scale_to_bounds(x, lower_bound, upper_bound):
10     """
11     Scale the input data so that it lies in between the lower and upper bounds.
12 
13     Args:
14         :attr:`x` (Tensor `n` or `b x n`):
15             the input
16         :attr:`lower_bound` (float)
17         :attr:`upper_bound` (float)
18 
19     Returns:
20         :obj:`torch.Tensor`
21     """
22     # Scale features so they fit inside grid bounds
23     min_val = x.min()
24     max_val = x.max()
25     diff = max_val - min_val
26     x = (x - min_val) * (0.95 * (upper_bound - lower_bound) / diff) + 0.95 * lower_bound
27     return x
28 
29 
30 def choose_grid_size(train_inputs, ratio=1.0, kronecker_structure=True):
31     """
32     Given some training inputs, determine a good grid size for KISS-GP.
33 
34     Args:
35         :attr:`train_inputs` (Tensor `n` or `n x d` or `b x n x d`):
36             training data
37         :attr:`ratio` (float, optional):
38             Ratio - number of grid points to the amount of data (default: 1.)
39         :attr:`kronecker_structure` (bool, default=True):
40             Whether or not the model will use Kronecker structure in the grid
41             (set to True unless there is an additive or product decomposition in the prior)
42 
43     Returns:
44         :obj:`int`
45     """
46     # Scale features so they fit inside grid bounds
47     num_data = train_inputs.numel() if train_inputs.dim() == 1 else train_inputs.size(-2)
48     num_dim = 1 if train_inputs.dim() == 1 else train_inputs.size(-1)
49     if kronecker_structure:
50         return int(ratio * math.pow(num_data, 1.0 / num_dim))
51     else:
52         return ratio * num_data
53 
54 
55 def convert_legacy_grid(grid: torch.Tensor) -> List[torch.Tensor]:
56     return [grid[:, i] for i in range(grid.size(-1))]
57 
58 
59 def create_data_from_grid(grid: List[torch.Tensor]) -> torch.Tensor:
60     """
61     Args:
62         :attr:`grid` (List[Tensor])
63             Each Tensor is a 1D set of increments for the grid in that dimension
64     Returns:
65         `grid_data` (Tensor)
66             Returns the set of points on the grid going by column-major order
67             (due to legacy reasons).
68     """
69     if torch.is_tensor(grid):
70         grid = convert_legacy_grid(grid)
71     ndims = len(grid)
72     assert all(axis.dim() == 1 for axis in grid)
73     projections = torch.meshgrid(*grid)
74     grid_tensor = torch.stack(projections, axis=-1)
75     # Note that if we did
76     #     grid_data = grid_tensor.reshape(-1, ndims)
77     # instead, we would be iterating through the points of our grid from the
78     # last data dimension to the first data dimension. However, due to legacy
79     # reasons, we need to iterate from the first data dimension to the last data
80     # dimension when creating grid_data
81     grid_data = grid_tensor.permute(*(reversed(range(ndims + 1)))).reshape(ndims, -1).transpose(0, 1)
82     return grid_data
83 
84 
85 def create_grid(
86     grid_sizes: List[int], grid_bounds: List[Tuple[float, float]], extend: bool = True, device="cpu", dtype=torch.float,
87 ) -> List[torch.Tensor]:
88     """
89     Creates a grid represented by a list of 1D Tensors representing the
90     projections of the grid into each dimension
91 
92     If `extend`, we extend the grid by two points past the specified boundary
93     which can be important for getting good grid interpolations
94     """
95     grid = []
96     for i in range(len(grid_bounds)):
97         grid_diff = float(grid_bounds[i][1] - grid_bounds[i][0]) / (grid_sizes[i] - 2)
98         if extend:
99             proj = torch.linspace(
100                 grid_bounds[i][0] - grid_diff, grid_bounds[i][1] + grid_diff, grid_sizes[i], device=device, dtype=dtype,
101             )
102         else:
103             proj = torch.linspace(grid_bounds[i][0], grid_bounds[i][1], grid_sizes[i], device=device, dtype=dtype,)
104         grid.append(proj)
105     return grid
106
```
--- 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/gpytorch/utils/grid.py b/gpytorch/utils/grid.py
--- a/gpytorch/utils/grid.py
+++ b/gpytorch/utils/grid.py
@@ -10,14 +10,12 @@
     """
     Scale the input data so that it lies in between the lower and upper bounds.
 
-    Args:
-        :attr:`x` (Tensor `n` or `b x n`):
-            the input
-        :attr:`lower_bound` (float)
-        :attr:`upper_bound` (float)
-
-    Returns:
-        :obj:`torch.Tensor`
+    :param x: the input data
+    :type x: torch.Tensor (... x n x d)
+    :param float lower_bound: lower bound of scaled data
+    :param float upper_bound: upper bound of scaled data
+    :return: scaled data
+    :rtype: torch.Tensor (... x n x d)
     """
     # Scale features so they fit inside grid bounds
     min_val = x.min()
@@ -31,17 +29,15 @@
     """
     Given some training inputs, determine a good grid size for KISS-GP.
 
-    Args:
-        :attr:`train_inputs` (Tensor `n` or `n x d` or `b x n x d`):
-            training data
-        :attr:`ratio` (float, optional):
-            Ratio - number of grid points to the amount of data (default: 1.)
-        :attr:`kronecker_structure` (bool, default=True):
-            Whether or not the model will use Kronecker structure in the grid
-            (set to True unless there is an additive or product decomposition in the prior)
-
-    Returns:
-        :obj:`int`
+    :param x: the input data
+    :type x: torch.Tensor (... x n x d)
+    :param ratio: Amount of grid points per data point (default: 1.)
+    :type ratio: float, optional
+    :param kronecker_structure: Whether or not the model will use Kronecker structure in the grid
+        (set to True unless there is an additive or product decomposition in the prior)
+    :type kronecker_structure: bool, optional
+    :return: Grid size
+    :rtype: int
     """
     # Scale features so they fit inside grid bounds
     num_data = train_inputs.numel() if train_inputs.dim() == 1 else train_inputs.size(-2)
@@ -58,13 +54,10 @@
 
 def create_data_from_grid(grid: List[torch.Tensor]) -> torch.Tensor:
     """
-    Args:
-        :attr:`grid` (List[Tensor])
-            Each Tensor is a 1D set of increments for the grid in that dimension
-    Returns:
-        `grid_data` (Tensor)
-            Returns the set of points on the grid going by column-major order
-            (due to legacy reasons).
+    :param grid: Each Tensor is a 1D set of increments for the grid in that dimension
+    :type grid: List[torch.Tensor]
+    :return: The set of points on the grid going by column-major order
+    :rtype: torch.Tensor
     """
     if torch.is_tensor(grid):
         grid = convert_legacy_grid(grid)
@@ -90,7 +83,18 @@
     projections of the grid into each dimension
 
     If `extend`, we extend the grid by two points past the specified boundary
-    which can be important for getting good grid interpolations
+    which can be important for getting good grid interpolations.
+
+    :param grid_sizes: Sizes of each grid dimension
+    :type grid_sizes: List[int]
+    :param grid_bounds: Lower and upper bounds of each grid dimension
+    :type grid_sizes: List[Tuple[float, float]]
+    :param device: target device for output (default: cpu)
+    :type device: torch.device, optional
+    :param dtype: target dtype for output (default: torch.float)
+    :type dtype: torch.dtype, optional
+    :return: Grid points for each dimension. Grid points are stored in a :obj:`torch.Tensor` with shape `grid_sizes[i]`.
+    :rtype: List[torch.Tensor]
     """
     grid = []
     for i in range(len(grid_bounds)):

{"golden_diff": "diff --git a/gpytorch/utils/grid.py b/gpytorch/utils/grid.py\n--- a/gpytorch/utils/grid.py\n+++ b/gpytorch/utils/grid.py\n@@ -10,14 +10,12 @@\n     \"\"\"\n     Scale the input data so that it lies in between the lower and upper bounds.\n \n-    Args:\n-        :attr:`x` (Tensor `n` or `b x n`):\n-            the input\n-        :attr:`lower_bound` (float)\n-        :attr:`upper_bound` (float)\n-\n-    Returns:\n-        :obj:`torch.Tensor`\n+    :param x: the input data\n+    :type x: torch.Tensor (... x n x d)\n+    :param float lower_bound: lower bound of scaled data\n+    :param float upper_bound: upper bound of scaled data\n+    :return: scaled data\n+    :rtype: torch.Tensor (... x n x d)\n     \"\"\"\n     # Scale features so they fit inside grid bounds\n     min_val = x.min()\n@@ -31,17 +29,15 @@\n     \"\"\"\n     Given some training inputs, determine a good grid size for KISS-GP.\n \n-    Args:\n-        :attr:`train_inputs` (Tensor `n` or `n x d` or `b x n x d`):\n-            training data\n-        :attr:`ratio` (float, optional):\n-            Ratio - number of grid points to the amount of data (default: 1.)\n-        :attr:`kronecker_structure` (bool, default=True):\n-            Whether or not the model will use Kronecker structure in the grid\n-            (set to True unless there is an additive or product decomposition in the prior)\n-\n-    Returns:\n-        :obj:`int`\n+    :param x: the input data\n+    :type x: torch.Tensor (... x n x d)\n+    :param ratio: Amount of grid points per data point (default: 1.)\n+    :type ratio: float, optional\n+    :param kronecker_structure: Whether or not the model will use Kronecker structure in the grid\n+        (set to True unless there is an additive or product decomposition in the prior)\n+    :type kronecker_structure: bool, optional\n+    :return: Grid size\n+    :rtype: int\n     \"\"\"\n     # Scale features so they fit inside grid bounds\n     num_data = train_inputs.numel() if train_inputs.dim() == 1 else train_inputs.size(-2)\n@@ -58,13 +54,10 @@\n \n def create_data_from_grid(grid: List[torch.Tensor]) -> torch.Tensor:\n     \"\"\"\n-    Args:\n-        :attr:`grid` (List[Tensor])\n-            Each Tensor is a 1D set of increments for the grid in that dimension\n-    Returns:\n-        `grid_data` (Tensor)\n-            Returns the set of points on the grid going by column-major order\n-            (due to legacy reasons).\n+    :param grid: Each Tensor is a 1D set of increments for the grid in that dimension\n+    :type grid: List[torch.Tensor]\n+    :return: The set of points on the grid going by column-major order\n+    :rtype: torch.Tensor\n     \"\"\"\n     if torch.is_tensor(grid):\n         grid = convert_legacy_grid(grid)\n@@ -90,7 +83,18 @@\n     projections of the grid into each dimension\n \n     If `extend`, we extend the grid by two points past the specified boundary\n-    which can be important for getting good grid interpolations\n+    which can be important for getting good grid interpolations.\n+\n+    :param grid_sizes: Sizes of each grid dimension\n+    :type grid_sizes: List[int]\n+    :param grid_bounds: Lower and upper bounds of each grid dimension\n+    :type grid_sizes: List[Tuple[float, float]]\n+    :param device: target device for output (default: cpu)\n+    :type device: torch.device, optional\n+    :param dtype: target dtype for output (default: torch.float)\n+    :type dtype: torch.dtype, optional\n+    :return: Grid points for each dimension. Grid points are stored in a :obj:`torch.Tensor` with shape `grid_sizes[i]`.\n+    :rtype: List[torch.Tensor]\n     \"\"\"\n     grid = []\n     for i in range(len(grid_bounds)):\n", "issue": "[Docs] Missing Grid.py documentation\n# \ud83d\udcda Documentation/Examples\r\n\r\n** Is there documentation missing? **\r\nThe utils section of [GPyTorch documentation](https://gpytorch.readthedocs.io) does not include any information on grid.py, which is referenced [elsewhere in the docs](https://docs.gpytorch.ai/en/stable/kernels.html?highlight=choose_grid_size#gpytorch.kernels.GridKernel.update_grid).\r\n\n", "before_files": [{"content": "#!/usr/bin/env python3\n\nimport math\nfrom typing import List, Tuple\n\nimport torch\n\n\ndef scale_to_bounds(x, lower_bound, upper_bound):\n    \"\"\"\n    Scale the input data so that it lies in between the lower and upper bounds.\n\n    Args:\n        :attr:`x` (Tensor `n` or `b x n`):\n            the input\n        :attr:`lower_bound` (float)\n        :attr:`upper_bound` (float)\n\n    Returns:\n        :obj:`torch.Tensor`\n    \"\"\"\n    # Scale features so they fit inside grid bounds\n    min_val = x.min()\n    max_val = x.max()\n    diff = max_val - min_val\n    x = (x - min_val) * (0.95 * (upper_bound - lower_bound) / diff) + 0.95 * lower_bound\n    return x\n\n\ndef choose_grid_size(train_inputs, ratio=1.0, kronecker_structure=True):\n    \"\"\"\n    Given some training inputs, determine a good grid size for KISS-GP.\n\n    Args:\n        :attr:`train_inputs` (Tensor `n` or `n x d` or `b x n x d`):\n            training data\n        :attr:`ratio` (float, optional):\n            Ratio - number of grid points to the amount of data (default: 1.)\n        :attr:`kronecker_structure` (bool, default=True):\n            Whether or not the model will use Kronecker structure in the grid\n            (set to True unless there is an additive or product decomposition in the prior)\n\n    Returns:\n        :obj:`int`\n    \"\"\"\n    # Scale features so they fit inside grid bounds\n    num_data = train_inputs.numel() if train_inputs.dim() == 1 else train_inputs.size(-2)\n    num_dim = 1 if train_inputs.dim() == 1 else train_inputs.size(-1)\n    if kronecker_structure:\n        return int(ratio * math.pow(num_data, 1.0 / num_dim))\n    else:\n        return ratio * num_data\n\n\ndef convert_legacy_grid(grid: torch.Tensor) -> List[torch.Tensor]:\n    return [grid[:, i] for i in range(grid.size(-1))]\n\n\ndef create_data_from_grid(grid: List[torch.Tensor]) -> torch.Tensor:\n    \"\"\"\n    Args:\n        :attr:`grid` (List[Tensor])\n            Each Tensor is a 1D set of increments for the grid in that dimension\n    Returns:\n        `grid_data` (Tensor)\n            Returns the set of points on the grid going by column-major order\n            (due to legacy reasons).\n    \"\"\"\n    if torch.is_tensor(grid):\n        grid = convert_legacy_grid(grid)\n    ndims = len(grid)\n    assert all(axis.dim() == 1 for axis in grid)\n    projections = torch.meshgrid(*grid)\n    grid_tensor = torch.stack(projections, axis=-1)\n    # Note that if we did\n    #     grid_data = grid_tensor.reshape(-1, ndims)\n    # instead, we would be iterating through the points of our grid from the\n    # last data dimension to the first data dimension. However, due to legacy\n    # reasons, we need to iterate from the first data dimension to the last data\n    # dimension when creating grid_data\n    grid_data = grid_tensor.permute(*(reversed(range(ndims + 1)))).reshape(ndims, -1).transpose(0, 1)\n    return grid_data\n\n\ndef create_grid(\n    grid_sizes: List[int], grid_bounds: List[Tuple[float, float]], extend: bool = True, device=\"cpu\", dtype=torch.float,\n) -> List[torch.Tensor]:\n    \"\"\"\n    Creates a grid represented by a list of 1D Tensors representing the\n    projections of the grid into each dimension\n\n    If `extend`, we extend the grid by two points past the specified boundary\n    which can be important for getting good grid interpolations\n    \"\"\"\n    grid = []\n    for i in range(len(grid_bounds)):\n        grid_diff = float(grid_bounds[i][1] - grid_bounds[i][0]) / (grid_sizes[i] - 2)\n        if extend:\n            proj = torch.linspace(\n                grid_bounds[i][0] - grid_diff, grid_bounds[i][1] + grid_diff, grid_sizes[i], device=device, dtype=dtype,\n            )\n        else:\n            proj = torch.linspace(grid_bounds[i][0], grid_bounds[i][1], grid_sizes[i], device=device, dtype=dtype,)\n        grid.append(proj)\n    return grid\n", "path": "gpytorch/utils/grid.py"}], "after_files": [{"content": "#!/usr/bin/env python3\n\nimport math\nfrom typing import List, Tuple\n\nimport torch\n\n\ndef scale_to_bounds(x, lower_bound, upper_bound):\n    \"\"\"\n    Scale the input data so that it lies in between the lower and upper bounds.\n\n    :param x: the input data\n    :type x: torch.Tensor (... x n x d)\n    :param float lower_bound: lower bound of scaled data\n    :param float upper_bound: upper bound of scaled data\n    :return: scaled data\n    :rtype: torch.Tensor (... x n x d)\n    \"\"\"\n    # Scale features so they fit inside grid bounds\n    min_val = x.min()\n    max_val = x.max()\n    diff = max_val - min_val\n    x = (x - min_val) * (0.95 * (upper_bound - lower_bound) / diff) + 0.95 * lower_bound\n    return x\n\n\ndef choose_grid_size(train_inputs, ratio=1.0, kronecker_structure=True):\n    \"\"\"\n    Given some training inputs, determine a good grid size for KISS-GP.\n\n    :param x: the input data\n    :type x: torch.Tensor (... x n x d)\n    :param ratio: Amount of grid points per data point (default: 1.)\n    :type ratio: float, optional\n    :param kronecker_structure: Whether or not the model will use Kronecker structure in the grid\n        (set to True unless there is an additive or product decomposition in the prior)\n    :type kronecker_structure: bool, optional\n    :return: Grid size\n    :rtype: int\n    \"\"\"\n    # Scale features so they fit inside grid bounds\n    num_data = train_inputs.numel() if train_inputs.dim() == 1 else train_inputs.size(-2)\n    num_dim = 1 if train_inputs.dim() == 1 else train_inputs.size(-1)\n    if kronecker_structure:\n        return int(ratio * math.pow(num_data, 1.0 / num_dim))\n    else:\n        return ratio * num_data\n\n\ndef convert_legacy_grid(grid: torch.Tensor) -> List[torch.Tensor]:\n    return [grid[:, i] for i in range(grid.size(-1))]\n\n\ndef create_data_from_grid(grid: List[torch.Tensor]) -> torch.Tensor:\n    \"\"\"\n    :param grid: Each Tensor is a 1D set of increments for the grid in that dimension\n    :type grid: List[torch.Tensor]\n    :return: The set of points on the grid going by column-major order\n    :rtype: torch.Tensor\n    \"\"\"\n    if torch.is_tensor(grid):\n        grid = convert_legacy_grid(grid)\n    ndims = len(grid)\n    assert all(axis.dim() == 1 for axis in grid)\n    projections = torch.meshgrid(*grid)\n    grid_tensor = torch.stack(projections, axis=-1)\n    # Note that if we did\n    #     grid_data = grid_tensor.reshape(-1, ndims)\n    # instead, we would be iterating through the points of our grid from the\n    # last data dimension to the first data dimension. However, due to legacy\n    # reasons, we need to iterate from the first data dimension to the last data\n    # dimension when creating grid_data\n    grid_data = grid_tensor.permute(*(reversed(range(ndims + 1)))).reshape(ndims, -1).transpose(0, 1)\n    return grid_data\n\n\ndef create_grid(\n    grid_sizes: List[int], grid_bounds: List[Tuple[float, float]], extend: bool = True, device=\"cpu\", dtype=torch.float,\n) -> List[torch.Tensor]:\n    \"\"\"\n    Creates a grid represented by a list of 1D Tensors representing the\n    projections of the grid into each dimension\n\n    If `extend`, we extend the grid by two points past the specified boundary\n    which can be important for getting good grid interpolations.\n\n    :param grid_sizes: Sizes of each grid dimension\n    :type grid_sizes: List[int]\n    :param grid_bounds: Lower and upper bounds of each grid dimension\n    :type grid_sizes: List[Tuple[float, float]]\n    :param device: target device for output (default: cpu)\n    :type device: torch.device, optional\n    :param dtype: target dtype for output (default: torch.float)\n    :type dtype: torch.dtype, optional\n    :return: Grid points for each dimension. Grid points are stored in a :obj:`torch.Tensor` with shape `grid_sizes[i]`.\n    :rtype: List[torch.Tensor]\n    \"\"\"\n    grid = []\n    for i in range(len(grid_bounds)):\n        grid_diff = float(grid_bounds[i][1] - grid_bounds[i][0]) / (grid_sizes[i] - 2)\n        if extend:\n            proj = torch.linspace(\n                grid_bounds[i][0] - grid_diff, grid_bounds[i][1] + grid_diff, grid_sizes[i], device=device, dtype=dtype,\n            )\n        else:\n            proj = torch.linspace(grid_bounds[i][0], grid_bounds[i][1], grid_sizes[i], device=device, dtype=dtype,)\n        grid.append(proj)\n    return grid\n", "path": "gpytorch/utils/grid.py"}]}