We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
PointCNN dilated KNN might select less than `K` neighbours 
## 🐛 Bug

In the current X-Conv implementation, I can see the following:

https://github.com/rusty1s/pytorch_geometric/blob/e8e004439e3204a7b888a21e508c45d166c5817c/torch_geometric/nn/conv/x_conv.py#L130-L137

As, `torch.randint` would sample with replacement, there's a chance that the number of actual neighbours can be less than `K`. I am thinking we can fix this using something like:

```
indices = torch.randperm(K * dil)[:K]
```

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `torch_geometric/nn/conv/x_conv.py`
Content:
```
1 from typing import Optional
2 
3 from math import ceil
4 
5 import torch
6 from torch import Tensor
7 from torch.nn import Sequential as S, Linear as L, BatchNorm1d as BN
8 from torch.nn import ELU, Conv1d
9 from torch_geometric.nn import Reshape
10 
11 from ..inits import reset
12 
13 try:
14     from torch_cluster import knn_graph
15 except ImportError:
16     knn_graph = None
17 
18 
19 class XConv(torch.nn.Module):
20     r"""The convolutional operator on :math:`\mathcal{X}`-transformed points
21     from the `"PointCNN: Convolution On X-Transformed Points"
22     <https://arxiv.org/abs/1801.07791>`_ paper
23 
24     .. math::
25         \mathbf{x}^{\prime}_i = \mathrm{Conv}\left(\mathbf{K},
26         \gamma_{\mathbf{\Theta}}(\mathbf{P}_i - \mathbf{p}_i) \times
27         \left( h_\mathbf{\Theta}(\mathbf{P}_i - \mathbf{p}_i) \, \Vert \,
28         \mathbf{x}_i \right) \right),
29 
30     where :math:`\mathbf{K}` and :math:`\mathbf{P}_i` denote the trainable
31     filter and neighboring point positions of :math:`\mathbf{x}_i`,
32     respectively.
33     :math:`\gamma_{\mathbf{\Theta}}` and :math:`h_{\mathbf{\Theta}}` describe
34     neural networks, *i.e.* MLPs, where :math:`h_{\mathbf{\Theta}}`
35     individually lifts each point into a higher-dimensional space, and
36     :math:`\gamma_{\mathbf{\Theta}}` computes the :math:`\mathcal{X}`-
37     transformation matrix based on *all* points in a neighborhood.
38 
39     Args:
40         in_channels (int): Size of each input sample.
41         out_channels (int): Size of each output sample.
42         dim (int): Point cloud dimensionality.
43         kernel_size (int): Size of the convolving kernel, *i.e.* number of
44             neighbors including self-loops.
45         hidden_channels (int, optional): Output size of
46             :math:`h_{\mathbf{\Theta}}`, *i.e.* dimensionality of lifted
47             points. If set to :obj:`None`, will be automatically set to
48             :obj:`in_channels / 4`. (default: :obj:`None`)
49         dilation (int, optional): The factor by which the neighborhood is
50             extended, from which :obj:`kernel_size` neighbors are then
51             uniformly sampled. Can be interpreted as the dilation rate of
52             classical convolutional operators. (default: :obj:`1`)
53         bias (bool, optional): If set to :obj:`False`, the layer will not learn
54             an additive bias. (default: :obj:`True`)
55         num_workers (int): Number of workers to use for k-NN computation.
56             Has no effect in case :obj:`batch` is not :obj:`None`, or the input
57             lies on the GPU. (default: :obj:`1`)
58     """
59     def __init__(self, in_channels: int, out_channels: int, dim: int,
60                  kernel_size: int, hidden_channels: Optional[int] = None,
61                  dilation: int = 1, bias: bool = True, num_workers: int = 1):
62         super(XConv, self).__init__()
63 
64         if knn_graph is None:
65             raise ImportError('`XConv` requires `torch-cluster`.')
66 
67         self.in_channels = in_channels
68         if hidden_channels is None:
69             hidden_channels = in_channels // 4
70         assert hidden_channels > 0
71         self.hidden_channels = hidden_channels
72         self.out_channels = out_channels
73         self.dim = dim
74         self.kernel_size = kernel_size
75         self.dilation = dilation
76         self.num_workers = num_workers
77 
78         C_in, C_delta, C_out = in_channels, hidden_channels, out_channels
79         D, K = dim, kernel_size
80 
81         self.mlp1 = S(
82             L(dim, C_delta),
83             ELU(),
84             BN(C_delta),
85             L(C_delta, C_delta),
86             ELU(),
87             BN(C_delta),
88             Reshape(-1, K, C_delta),
89         )
90 
91         self.mlp2 = S(
92             L(D * K, K**2),
93             ELU(),
94             BN(K**2),
95             Reshape(-1, K, K),
96             Conv1d(K, K**2, K, groups=K),
97             ELU(),
98             BN(K**2),
99             Reshape(-1, K, K),
100             Conv1d(K, K**2, K, groups=K),
101             BN(K**2),
102             Reshape(-1, K, K),
103         )
104 
105         C_in = C_in + C_delta
106         depth_multiplier = int(ceil(C_out / C_in))
107         self.conv = S(
108             Conv1d(C_in, C_in * depth_multiplier, K, groups=C_in),
109             Reshape(-1, C_in * depth_multiplier),
110             L(C_in * depth_multiplier, C_out, bias=bias),
111         )
112 
113         self.reset_parameters()
114 
115     def reset_parameters(self):
116         reset(self.mlp1)
117         reset(self.mlp2)
118         reset(self.conv)
119 
120     def forward(self, x: Tensor, pos: Tensor, batch: Optional[Tensor] = None):
121         """"""
122         pos = pos.unsqueeze(-1) if pos.dim() == 1 else pos
123         (N, D), K = pos.size(), self.kernel_size
124 
125         edge_index = knn_graph(pos, K * self.dilation, batch, loop=True,
126                                flow='target_to_source',
127                                num_workers=self.num_workers)
128         row, col = edge_index[0], edge_index[1]
129 
130         if self.dilation > 1:
131             dil = self.dilation
132             index = torch.randint(K * dil, (N, K), dtype=torch.long,
133                                   device=row.device)
134             arange = torch.arange(N, dtype=torch.long, device=row.device)
135             arange = arange * (K * dil)
136             index = (index + arange.view(-1, 1)).view(-1)
137             row, col = row[index], col[index]
138 
139         pos = pos[col] - pos[row]
140 
141         x_star = self.mlp1(pos)
142         if x is not None:
143             x = x.unsqueeze(-1) if x.dim() == 1 else x
144             x = x[col].view(N, K, self.in_channels)
145             x_star = torch.cat([x_star, x], dim=-1)
146         x_star = x_star.transpose(1, 2).contiguous()
147 
148         transform_matrix = self.mlp2(pos.view(N, K * D))
149 
150         x_transformed = torch.matmul(x_star, transform_matrix)
151 
152         out = self.conv(x_transformed)
153 
154         return out
155 
156     def __repr__(self):
157         return '{}({}, {})'.format(self.__class__.__name__, self.in_channels,
158                                    self.out_channels)
159
```
--- 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/torch_geometric/nn/conv/x_conv.py b/torch_geometric/nn/conv/x_conv.py
--- a/torch_geometric/nn/conv/x_conv.py
+++ b/torch_geometric/nn/conv/x_conv.py
@@ -125,16 +125,11 @@
         edge_index = knn_graph(pos, K * self.dilation, batch, loop=True,
                                flow='target_to_source',
                                num_workers=self.num_workers)
-        row, col = edge_index[0], edge_index[1]
 
         if self.dilation > 1:
-            dil = self.dilation
-            index = torch.randint(K * dil, (N, K), dtype=torch.long,
-                                  device=row.device)
-            arange = torch.arange(N, dtype=torch.long, device=row.device)
-            arange = arange * (K * dil)
-            index = (index + arange.view(-1, 1)).view(-1)
-            row, col = row[index], col[index]
+            edge_index = edge_index[:, ::K]
+
+        row, col = edge_index[0], edge_index[1]
 
         pos = pos[col] - pos[row]

{"golden_diff": "diff --git a/torch_geometric/nn/conv/x_conv.py b/torch_geometric/nn/conv/x_conv.py\n--- a/torch_geometric/nn/conv/x_conv.py\n+++ b/torch_geometric/nn/conv/x_conv.py\n@@ -125,16 +125,11 @@\n         edge_index = knn_graph(pos, K * self.dilation, batch, loop=True,\n                                flow='target_to_source',\n                                num_workers=self.num_workers)\n-        row, col = edge_index[0], edge_index[1]\n \n         if self.dilation > 1:\n-            dil = self.dilation\n-            index = torch.randint(K * dil, (N, K), dtype=torch.long,\n-                                  device=row.device)\n-            arange = torch.arange(N, dtype=torch.long, device=row.device)\n-            arange = arange * (K * dil)\n-            index = (index + arange.view(-1, 1)).view(-1)\n-            row, col = row[index], col[index]\n+            edge_index = edge_index[:, ::K]\n+\n+        row, col = edge_index[0], edge_index[1]\n \n         pos = pos[col] - pos[row]\n", "issue": "PointCNN dilated KNN might select less than `K` neighbours \n## \ud83d\udc1b Bug\r\n\r\nIn the current X-Conv implementation, I can see the following:\r\n\r\nhttps://github.com/rusty1s/pytorch_geometric/blob/e8e004439e3204a7b888a21e508c45d166c5817c/torch_geometric/nn/conv/x_conv.py#L130-L137\r\n\r\nAs, `torch.randint` would sample with replacement, there's a chance that the number of actual neighbours can be less than `K`. I am thinking we can fix this using something like:\r\n\r\n```\r\nindices = torch.randperm(K * dil)[:K]\r\n```\n", "before_files": [{"content": "from typing import Optional\n\nfrom math import ceil\n\nimport torch\nfrom torch import Tensor\nfrom torch.nn import Sequential as S, Linear as L, BatchNorm1d as BN\nfrom torch.nn import ELU, Conv1d\nfrom torch_geometric.nn import Reshape\n\nfrom ..inits import reset\n\ntry:\n    from torch_cluster import knn_graph\nexcept ImportError:\n    knn_graph = None\n\n\nclass XConv(torch.nn.Module):\n    r\"\"\"The convolutional operator on :math:`\\mathcal{X}`-transformed points\n    from the `\"PointCNN: Convolution On X-Transformed Points\"\n    <https://arxiv.org/abs/1801.07791>`_ paper\n\n    .. math::\n        \\mathbf{x}^{\\prime}_i = \\mathrm{Conv}\\left(\\mathbf{K},\n        \\gamma_{\\mathbf{\\Theta}}(\\mathbf{P}_i - \\mathbf{p}_i) \\times\n        \\left( h_\\mathbf{\\Theta}(\\mathbf{P}_i - \\mathbf{p}_i) \\, \\Vert \\,\n        \\mathbf{x}_i \\right) \\right),\n\n    where :math:`\\mathbf{K}` and :math:`\\mathbf{P}_i` denote the trainable\n    filter and neighboring point positions of :math:`\\mathbf{x}_i`,\n    respectively.\n    :math:`\\gamma_{\\mathbf{\\Theta}}` and :math:`h_{\\mathbf{\\Theta}}` describe\n    neural networks, *i.e.* MLPs, where :math:`h_{\\mathbf{\\Theta}}`\n    individually lifts each point into a higher-dimensional space, and\n    :math:`\\gamma_{\\mathbf{\\Theta}}` computes the :math:`\\mathcal{X}`-\n    transformation matrix based on *all* points in a neighborhood.\n\n    Args:\n        in_channels (int): Size of each input sample.\n        out_channels (int): Size of each output sample.\n        dim (int): Point cloud dimensionality.\n        kernel_size (int): Size of the convolving kernel, *i.e.* number of\n            neighbors including self-loops.\n        hidden_channels (int, optional): Output size of\n            :math:`h_{\\mathbf{\\Theta}}`, *i.e.* dimensionality of lifted\n            points. If set to :obj:`None`, will be automatically set to\n            :obj:`in_channels / 4`. (default: :obj:`None`)\n        dilation (int, optional): The factor by which the neighborhood is\n            extended, from which :obj:`kernel_size` neighbors are then\n            uniformly sampled. Can be interpreted as the dilation rate of\n            classical convolutional operators. (default: :obj:`1`)\n        bias (bool, optional): If set to :obj:`False`, the layer will not learn\n            an additive bias. (default: :obj:`True`)\n        num_workers (int): Number of workers to use for k-NN computation.\n            Has no effect in case :obj:`batch` is not :obj:`None`, or the input\n            lies on the GPU. (default: :obj:`1`)\n    \"\"\"\n    def __init__(self, in_channels: int, out_channels: int, dim: int,\n                 kernel_size: int, hidden_channels: Optional[int] = None,\n                 dilation: int = 1, bias: bool = True, num_workers: int = 1):\n        super(XConv, self).__init__()\n\n        if knn_graph is None:\n            raise ImportError('`XConv` requires `torch-cluster`.')\n\n        self.in_channels = in_channels\n        if hidden_channels is None:\n            hidden_channels = in_channels // 4\n        assert hidden_channels > 0\n        self.hidden_channels = hidden_channels\n        self.out_channels = out_channels\n        self.dim = dim\n        self.kernel_size = kernel_size\n        self.dilation = dilation\n        self.num_workers = num_workers\n\n        C_in, C_delta, C_out = in_channels, hidden_channels, out_channels\n        D, K = dim, kernel_size\n\n        self.mlp1 = S(\n            L(dim, C_delta),\n            ELU(),\n            BN(C_delta),\n            L(C_delta, C_delta),\n            ELU(),\n            BN(C_delta),\n            Reshape(-1, K, C_delta),\n        )\n\n        self.mlp2 = S(\n            L(D * K, K**2),\n            ELU(),\n            BN(K**2),\n            Reshape(-1, K, K),\n            Conv1d(K, K**2, K, groups=K),\n            ELU(),\n            BN(K**2),\n            Reshape(-1, K, K),\n            Conv1d(K, K**2, K, groups=K),\n            BN(K**2),\n            Reshape(-1, K, K),\n        )\n\n        C_in = C_in + C_delta\n        depth_multiplier = int(ceil(C_out / C_in))\n        self.conv = S(\n            Conv1d(C_in, C_in * depth_multiplier, K, groups=C_in),\n            Reshape(-1, C_in * depth_multiplier),\n            L(C_in * depth_multiplier, C_out, bias=bias),\n        )\n\n        self.reset_parameters()\n\n    def reset_parameters(self):\n        reset(self.mlp1)\n        reset(self.mlp2)\n        reset(self.conv)\n\n    def forward(self, x: Tensor, pos: Tensor, batch: Optional[Tensor] = None):\n        \"\"\"\"\"\"\n        pos = pos.unsqueeze(-1) if pos.dim() == 1 else pos\n        (N, D), K = pos.size(), self.kernel_size\n\n        edge_index = knn_graph(pos, K * self.dilation, batch, loop=True,\n                               flow='target_to_source',\n                               num_workers=self.num_workers)\n        row, col = edge_index[0], edge_index[1]\n\n        if self.dilation > 1:\n            dil = self.dilation\n            index = torch.randint(K * dil, (N, K), dtype=torch.long,\n                                  device=row.device)\n            arange = torch.arange(N, dtype=torch.long, device=row.device)\n            arange = arange * (K * dil)\n            index = (index + arange.view(-1, 1)).view(-1)\n            row, col = row[index], col[index]\n\n        pos = pos[col] - pos[row]\n\n        x_star = self.mlp1(pos)\n        if x is not None:\n            x = x.unsqueeze(-1) if x.dim() == 1 else x\n            x = x[col].view(N, K, self.in_channels)\n            x_star = torch.cat([x_star, x], dim=-1)\n        x_star = x_star.transpose(1, 2).contiguous()\n\n        transform_matrix = self.mlp2(pos.view(N, K * D))\n\n        x_transformed = torch.matmul(x_star, transform_matrix)\n\n        out = self.conv(x_transformed)\n\n        return out\n\n    def __repr__(self):\n        return '{}({}, {})'.format(self.__class__.__name__, self.in_channels,\n                                   self.out_channels)\n", "path": "torch_geometric/nn/conv/x_conv.py"}], "after_files": [{"content": "from typing import Optional\n\nfrom math import ceil\n\nimport torch\nfrom torch import Tensor\nfrom torch.nn import Sequential as S, Linear as L, BatchNorm1d as BN\nfrom torch.nn import ELU, Conv1d\nfrom torch_geometric.nn import Reshape\n\nfrom ..inits import reset\n\ntry:\n    from torch_cluster import knn_graph\nexcept ImportError:\n    knn_graph = None\n\n\nclass XConv(torch.nn.Module):\n    r\"\"\"The convolutional operator on :math:`\\mathcal{X}`-transformed points\n    from the `\"PointCNN: Convolution On X-Transformed Points\"\n    <https://arxiv.org/abs/1801.07791>`_ paper\n\n    .. math::\n        \\mathbf{x}^{\\prime}_i = \\mathrm{Conv}\\left(\\mathbf{K},\n        \\gamma_{\\mathbf{\\Theta}}(\\mathbf{P}_i - \\mathbf{p}_i) \\times\n        \\left( h_\\mathbf{\\Theta}(\\mathbf{P}_i - \\mathbf{p}_i) \\, \\Vert \\,\n        \\mathbf{x}_i \\right) \\right),\n\n    where :math:`\\mathbf{K}` and :math:`\\mathbf{P}_i` denote the trainable\n    filter and neighboring point positions of :math:`\\mathbf{x}_i`,\n    respectively.\n    :math:`\\gamma_{\\mathbf{\\Theta}}` and :math:`h_{\\mathbf{\\Theta}}` describe\n    neural networks, *i.e.* MLPs, where :math:`h_{\\mathbf{\\Theta}}`\n    individually lifts each point into a higher-dimensional space, and\n    :math:`\\gamma_{\\mathbf{\\Theta}}` computes the :math:`\\mathcal{X}`-\n    transformation matrix based on *all* points in a neighborhood.\n\n    Args:\n        in_channels (int): Size of each input sample.\n        out_channels (int): Size of each output sample.\n        dim (int): Point cloud dimensionality.\n        kernel_size (int): Size of the convolving kernel, *i.e.* number of\n            neighbors including self-loops.\n        hidden_channels (int, optional): Output size of\n            :math:`h_{\\mathbf{\\Theta}}`, *i.e.* dimensionality of lifted\n            points. If set to :obj:`None`, will be automatically set to\n            :obj:`in_channels / 4`. (default: :obj:`None`)\n        dilation (int, optional): The factor by which the neighborhood is\n            extended, from which :obj:`kernel_size` neighbors are then\n            uniformly sampled. Can be interpreted as the dilation rate of\n            classical convolutional operators. (default: :obj:`1`)\n        bias (bool, optional): If set to :obj:`False`, the layer will not learn\n            an additive bias. (default: :obj:`True`)\n        num_workers (int): Number of workers to use for k-NN computation.\n            Has no effect in case :obj:`batch` is not :obj:`None`, or the input\n            lies on the GPU. (default: :obj:`1`)\n    \"\"\"\n    def __init__(self, in_channels: int, out_channels: int, dim: int,\n                 kernel_size: int, hidden_channels: Optional[int] = None,\n                 dilation: int = 1, bias: bool = True, num_workers: int = 1):\n        super(XConv, self).__init__()\n\n        if knn_graph is None:\n            raise ImportError('`XConv` requires `torch-cluster`.')\n\n        self.in_channels = in_channels\n        if hidden_channels is None:\n            hidden_channels = in_channels // 4\n        assert hidden_channels > 0\n        self.hidden_channels = hidden_channels\n        self.out_channels = out_channels\n        self.dim = dim\n        self.kernel_size = kernel_size\n        self.dilation = dilation\n        self.num_workers = num_workers\n\n        C_in, C_delta, C_out = in_channels, hidden_channels, out_channels\n        D, K = dim, kernel_size\n\n        self.mlp1 = S(\n            L(dim, C_delta),\n            ELU(),\n            BN(C_delta),\n            L(C_delta, C_delta),\n            ELU(),\n            BN(C_delta),\n            Reshape(-1, K, C_delta),\n        )\n\n        self.mlp2 = S(\n            L(D * K, K**2),\n            ELU(),\n            BN(K**2),\n            Reshape(-1, K, K),\n            Conv1d(K, K**2, K, groups=K),\n            ELU(),\n            BN(K**2),\n            Reshape(-1, K, K),\n            Conv1d(K, K**2, K, groups=K),\n            BN(K**2),\n            Reshape(-1, K, K),\n        )\n\n        C_in = C_in + C_delta\n        depth_multiplier = int(ceil(C_out / C_in))\n        self.conv = S(\n            Conv1d(C_in, C_in * depth_multiplier, K, groups=C_in),\n            Reshape(-1, C_in * depth_multiplier),\n            L(C_in * depth_multiplier, C_out, bias=bias),\n        )\n\n        self.reset_parameters()\n\n    def reset_parameters(self):\n        reset(self.mlp1)\n        reset(self.mlp2)\n        reset(self.conv)\n\n    def forward(self, x: Tensor, pos: Tensor, batch: Optional[Tensor] = None):\n        \"\"\"\"\"\"\n        pos = pos.unsqueeze(-1) if pos.dim() == 1 else pos\n        (N, D), K = pos.size(), self.kernel_size\n\n        edge_index = knn_graph(pos, K * self.dilation, batch, loop=True,\n                               flow='target_to_source',\n                               num_workers=self.num_workers)\n\n        if self.dilation > 1:\n            edge_index = edge_index[:, ::K]\n\n        row, col = edge_index[0], edge_index[1]\n\n        pos = pos[col] - pos[row]\n\n        x_star = self.mlp1(pos)\n        if x is not None:\n            x = x.unsqueeze(-1) if x.dim() == 1 else x\n            x = x[col].view(N, K, self.in_channels)\n            x_star = torch.cat([x_star, x], dim=-1)\n        x_star = x_star.transpose(1, 2).contiguous()\n\n        transform_matrix = self.mlp2(pos.view(N, K * D))\n\n        x_transformed = torch.matmul(x_star, transform_matrix)\n\n        out = self.conv(x_transformed)\n\n        return out\n\n    def __repr__(self):\n        return '{}({}, {})'.format(self.__class__.__name__, self.in_channels,\n                                   self.out_channels)\n", "path": "torch_geometric/nn/conv/x_conv.py"}]}