We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
[Example][Bug] Running error on the example case: example/pytorch/dimenet
## 🐛 Bug

Dimenet example is crashed. 

## To Reproduce

`python main.py --model-cnf config/dimenet.yaml`

```
Traceback (most recent call last):
  File "main.py", line 254, in <module>
    main()
  File "/opt/conda/lib/python3.8/site-packages/click/core.py", line 1128, in __call__
    return self.main(*args, **kwargs)
  File "/opt/conda/lib/python3.8/site-packages/click/core.py", line 1053, in main
    rv = self.invoke(ctx)
  File "/opt/conda/lib/python3.8/site-packages/click/core.py", line 1395, in invoke
    return ctx.invoke(self.callback, **ctx.params)
  File "/opt/conda/lib/python3.8/site-packages/click/core.py", line 754, in invoke
    return __callback(*args, **kwargs)
  File "main.py", line 165, in main
    model = DimeNet(emb_size=model_params['emb_size'],
  File "/workspace/examples/dimenet/modules/dimenet.py", line 64, in __init__
    self.rbf_layer = BesselBasisLayer(num_radial=num_radial,
  File "/workspace/examples/dimenet/modules/bessel_basis_layer.py", line 17, in __init__
    self.reset_params()
  File "/workspace/examples/dimenet/modules/bessel_basis_layer.py", line 20, in reset_params
    torch.arange(1, self.frequencies.numel() + 1, out=self.frequencies).mul_(np.pi)
RuntimeError: a leaf Variable that requires grad is being used in an in-place operation.
```

## Expected behavior

The case should run through

## Environment

 - DGL Version (e.g., 1.0): 0.9
 - Backend Library & Version (e.g., PyTorch 0.4.1, MXNet/Gluon 1.3): 1.12
 - OS (e.g., Linux): ubuntu
 - How you installed DGL (`conda`, `pip`, source): source
 - Build command you used (if compiling from source):
 - Python version: 3.8
 - CUDA/cuDNN version (if applicable): 11.7
 - GPU models and configuration (e.g. V100): A100
 - Any other relevant information:



--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `examples/pytorch/dimenet/modules/bessel_basis_layer.py`
Content:
```
1 import numpy as np
2 import torch
3 import torch.nn as nn
4 
5 from modules.envelope import Envelope
6 
7 class BesselBasisLayer(nn.Module):
8     def __init__(self,
9                  num_radial,
10                  cutoff,
11                  envelope_exponent=5):
12         super(BesselBasisLayer, self).__init__()
13         
14         self.cutoff = cutoff
15         self.envelope = Envelope(envelope_exponent)
16         self.frequencies = nn.Parameter(torch.Tensor(num_radial))
17         self.reset_params()
18 
19     def reset_params(self):
20         torch.arange(1, self.frequencies.numel() + 1, out=self.frequencies).mul_(np.pi)
21 
22     def forward(self, g):
23         d_scaled = g.edata['d'] / self.cutoff
24         # Necessary for proper broadcasting behaviour
25         d_scaled = torch.unsqueeze(d_scaled, -1)
26         d_cutoff = self.envelope(d_scaled)
27         g.edata['rbf'] = d_cutoff * torch.sin(self.frequencies * d_scaled)
28         return g
```
--- 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/examples/pytorch/dimenet/modules/bessel_basis_layer.py b/examples/pytorch/dimenet/modules/bessel_basis_layer.py
--- a/examples/pytorch/dimenet/modules/bessel_basis_layer.py
+++ b/examples/pytorch/dimenet/modules/bessel_basis_layer.py
@@ -17,7 +17,9 @@
         self.reset_params()
 
     def reset_params(self):
-        torch.arange(1, self.frequencies.numel() + 1, out=self.frequencies).mul_(np.pi)
+        with torch.no_grad():
+            torch.arange(1, self.frequencies.numel() + 1, out=self.frequencies).mul_(np.pi)
+        self.frequencies.requires_grad_()
 
     def forward(self, g):
         d_scaled = g.edata['d'] / self.cutoff
@@ -25,4 +27,4 @@
         d_scaled = torch.unsqueeze(d_scaled, -1)
         d_cutoff = self.envelope(d_scaled)
         g.edata['rbf'] = d_cutoff * torch.sin(self.frequencies * d_scaled)
-        return g
\ No newline at end of file
+        return g

{"golden_diff": "diff --git a/examples/pytorch/dimenet/modules/bessel_basis_layer.py b/examples/pytorch/dimenet/modules/bessel_basis_layer.py\n--- a/examples/pytorch/dimenet/modules/bessel_basis_layer.py\n+++ b/examples/pytorch/dimenet/modules/bessel_basis_layer.py\n@@ -17,7 +17,9 @@\n         self.reset_params()\n \n     def reset_params(self):\n-        torch.arange(1, self.frequencies.numel() + 1, out=self.frequencies).mul_(np.pi)\n+        with torch.no_grad():\n+            torch.arange(1, self.frequencies.numel() + 1, out=self.frequencies).mul_(np.pi)\n+        self.frequencies.requires_grad_()\n \n     def forward(self, g):\n         d_scaled = g.edata['d'] / self.cutoff\n@@ -25,4 +27,4 @@\n         d_scaled = torch.unsqueeze(d_scaled, -1)\n         d_cutoff = self.envelope(d_scaled)\n         g.edata['rbf'] = d_cutoff * torch.sin(self.frequencies * d_scaled)\n-        return g\n\\ No newline at end of file\n+        return g\n", "issue": "[Example][Bug] Running error on the example case: example/pytorch/dimenet\n## \ud83d\udc1b Bug\r\n\r\nDimenet example is crashed. \r\n\r\n## To Reproduce\r\n\r\n`python main.py --model-cnf config/dimenet.yaml`\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"main.py\", line 254, in <module>\r\n    main()\r\n  File \"/opt/conda/lib/python3.8/site-packages/click/core.py\", line 1128, in __call__\r\n    return self.main(*args, **kwargs)\r\n  File \"/opt/conda/lib/python3.8/site-packages/click/core.py\", line 1053, in main\r\n    rv = self.invoke(ctx)\r\n  File \"/opt/conda/lib/python3.8/site-packages/click/core.py\", line 1395, in invoke\r\n    return ctx.invoke(self.callback, **ctx.params)\r\n  File \"/opt/conda/lib/python3.8/site-packages/click/core.py\", line 754, in invoke\r\n    return __callback(*args, **kwargs)\r\n  File \"main.py\", line 165, in main\r\n    model = DimeNet(emb_size=model_params['emb_size'],\r\n  File \"/workspace/examples/dimenet/modules/dimenet.py\", line 64, in __init__\r\n    self.rbf_layer = BesselBasisLayer(num_radial=num_radial,\r\n  File \"/workspace/examples/dimenet/modules/bessel_basis_layer.py\", line 17, in __init__\r\n    self.reset_params()\r\n  File \"/workspace/examples/dimenet/modules/bessel_basis_layer.py\", line 20, in reset_params\r\n    torch.arange(1, self.frequencies.numel() + 1, out=self.frequencies).mul_(np.pi)\r\nRuntimeError: a leaf Variable that requires grad is being used in an in-place operation.\r\n```\r\n\r\n## Expected behavior\r\n\r\nThe case should run through\r\n\r\n## Environment\r\n\r\n - DGL Version (e.g., 1.0): 0.9\r\n - Backend Library & Version (e.g., PyTorch 0.4.1, MXNet/Gluon 1.3): 1.12\r\n - OS (e.g., Linux): ubuntu\r\n - How you installed DGL (`conda`, `pip`, source): source\r\n - Build command you used (if compiling from source):\r\n - Python version: 3.8\r\n - CUDA/cuDNN version (if applicable): 11.7\r\n - GPU models and configuration (e.g. V100): A100\r\n - Any other relevant information:\r\n\r\n\n", "before_files": [{"content": "import numpy as np\nimport torch\nimport torch.nn as nn\n\nfrom modules.envelope import Envelope\n\nclass BesselBasisLayer(nn.Module):\n    def __init__(self,\n                 num_radial,\n                 cutoff,\n                 envelope_exponent=5):\n        super(BesselBasisLayer, self).__init__()\n        \n        self.cutoff = cutoff\n        self.envelope = Envelope(envelope_exponent)\n        self.frequencies = nn.Parameter(torch.Tensor(num_radial))\n        self.reset_params()\n\n    def reset_params(self):\n        torch.arange(1, self.frequencies.numel() + 1, out=self.frequencies).mul_(np.pi)\n\n    def forward(self, g):\n        d_scaled = g.edata['d'] / self.cutoff\n        # Necessary for proper broadcasting behaviour\n        d_scaled = torch.unsqueeze(d_scaled, -1)\n        d_cutoff = self.envelope(d_scaled)\n        g.edata['rbf'] = d_cutoff * torch.sin(self.frequencies * d_scaled)\n        return g", "path": "examples/pytorch/dimenet/modules/bessel_basis_layer.py"}], "after_files": [{"content": "import numpy as np\nimport torch\nimport torch.nn as nn\n\nfrom modules.envelope import Envelope\n\nclass BesselBasisLayer(nn.Module):\n    def __init__(self,\n                 num_radial,\n                 cutoff,\n                 envelope_exponent=5):\n        super(BesselBasisLayer, self).__init__()\n        \n        self.cutoff = cutoff\n        self.envelope = Envelope(envelope_exponent)\n        self.frequencies = nn.Parameter(torch.Tensor(num_radial))\n        self.reset_params()\n\n    def reset_params(self):\n        with torch.no_grad():\n            torch.arange(1, self.frequencies.numel() + 1, out=self.frequencies).mul_(np.pi)\n        self.frequencies.requires_grad_()\n\n    def forward(self, g):\n        d_scaled = g.edata['d'] / self.cutoff\n        # Necessary for proper broadcasting behaviour\n        d_scaled = torch.unsqueeze(d_scaled, -1)\n        d_cutoff = self.envelope(d_scaled)\n        g.edata['rbf'] = d_cutoff * torch.sin(self.frequencies * d_scaled)\n        return g\n", "path": "examples/pytorch/dimenet/modules/bessel_basis_layer.py"}]}