Errors I faced while attempting run

[rodroadl]

Hi, thanks for sharing your code here. I have attempted to run below code from your answer in issue #1 :

import torch
from torch_geometric.data import Data, Batch

model = PointGNN_Normalization()

data_list = [Data(x=torch.tensor([[-1], [0], [1]], dtype=torch.float), 
            edge_index=torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]], dtype=torch.long))]

batch = Batch.from_data_list(data_list)

out = model(batch)

And was wondering if current version is working, since I have faced some error.

1.

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
Cell In[1], [line 12](vscode-notebook-cell:?execution_count=1&line=12)
      [7](vscode-notebook-cell:?execution_count=1&line=7) data_list = [Data(x=torch.tensor([[-1], [0], [1]], dtype=torch.float), 
      [8](vscode-notebook-cell:?execution_count=1&line=8)             edge_index=torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]], dtype=torch.long))]
     [10](vscode-notebook-cell:?execution_count=1&line=10) batch = Batch.from_data_list(data_list)
---> [12](vscode-notebook-cell:?execution_count=1&line=12) out = model(batch)

File c:\Users\kkgg3\.conda\envs\tdl\Lib\site-packages\torch\nn\modules\module.py:1532, in Module._wrapped_call_impl(self, *args, **kwargs)
   [1530](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1530)     return self._compiled_call_impl(*args, **kwargs)  # type: ignore[misc]
   [1531](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1531) else:
-> [1532](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1532)     return self._call_impl(*args, **kwargs)

File c:\Users\kkgg3\.conda\envs\tdl\Lib\site-packages\torch\nn\modules\module.py:1541, in Module._call_impl(self, *args, **kwargs)
   [1536](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1536) # If we don't have any hooks, we want to skip the rest of the logic in
   [1537](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1537) # this function, and just call forward.
   [1538](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1538) if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
   [1539](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1539)         or _global_backward_pre_hooks or _global_backward_hooks
   [1540](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1540)         or _global_forward_hooks or _global_forward_pre_hooks):
-> [1541](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1541)     return forward_call(*args, **kwargs)
   [1543](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1543) try:
   [1544](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1544)     result = None

File c:\Users\kkgg3\point-gnn-pytorch\src\point_gnn_pytorch\models.py:143, in PointGNN_Normalization.forward(self, data)
    [140](file:///C:/Users/kkgg3/point-gnn-pytorch/src/point_gnn_pytorch/models.py:140) pos = x
    [142](file:///C:/Users/kkgg3/point-gnn-pytorch/src/point_gnn_pytorch/models.py:142) # 1. do projection into high-dimensional space
--> [143](file:///C:/Users/kkgg3/point-gnn-pytorch/src/point_gnn_pytorch/models.py:143) x = self.project(x)
    [144](file:///C:/Users/kkgg3/point-gnn-pytorch/src/point_gnn_pytorch/models.py:144) x = F.leaky_relu(x)
    [146](file:///C:/Users/kkgg3/point-gnn-pytorch/src/point_gnn_pytorch/models.py:146) # 2. apply various PointGNN convolutions 

File c:\Users\kkgg3\.conda\envs\tdl\Lib\site-packages\torch\nn\modules\module.py:1532, in Module._wrapped_call_impl(self, *args, **kwargs)
   [1530](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1530)     return self._compiled_call_impl(*args, **kwargs)  # type: ignore[misc]
   [1531](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1531) else:
-> [1532](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1532)     return self._call_impl(*args, **kwargs)

File c:\Users\kkgg3\.conda\envs\tdl\Lib\site-packages\torch\nn\modules\module.py:1541, in Module._call_impl(self, *args, **kwargs)
   [1536](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1536) # If we don't have any hooks, we want to skip the rest of the logic in
   [1537](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1537) # this function, and just call forward.
   [1538](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1538) if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
   [1539](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1539)         or _global_backward_pre_hooks or _global_backward_hooks
   [1540](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1540)         or _global_forward_hooks or _global_forward_pre_hooks):
-> [1541](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1541)     return forward_call(*args, **kwargs)
   [1543](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1543) try:
   [1544](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1544)     result = None

File c:\Users\kkgg3\.conda\envs\tdl\Lib\site-packages\torch_geometric\nn\models\mlp.py:231, in MLP.forward(self, x, batch, batch_size, return_emb)
    [228](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch_geometric/nn/models/mlp.py:228) # If `plain_last=True`, then `len(norms) = len(lins) -1, thus skipping
    [229](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch_geometric/nn/models/mlp.py:229) # the execution of the last layer inside the for-loop.
    [230](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch_geometric/nn/models/mlp.py:230) for i, (lin, norm) in enumerate(zip(self.lins, self.norms)):
--> [231](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch_geometric/nn/models/mlp.py:231)     x = lin(x)
    [232](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch_geometric/nn/models/mlp.py:232)     if self.act is not None and self.act_first:
    [233](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch_geometric/nn/models/mlp.py:233)         x = self.act(x)

File c:\Users\kkgg3\.conda\envs\tdl\Lib\site-packages\torch\nn\modules\module.py:1532, in Module._wrapped_call_impl(self, *args, **kwargs)
   [1530](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1530)     return self._compiled_call_impl(*args, **kwargs)  # type: ignore[misc]
   [1531](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1531) else:
-> [1532](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1532)     return self._call_impl(*args, **kwargs)

File c:\Users\kkgg3\.conda\envs\tdl\Lib\site-packages\torch\nn\modules\module.py:1541, in Module._call_impl(self, *args, **kwargs)
   [1536](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1536) # If we don't have any hooks, we want to skip the rest of the logic in
   [1537](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1537) # this function, and just call forward.
   [1538](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1538) if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
   [1539](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1539)         or _global_backward_pre_hooks or _global_backward_hooks
   [1540](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1540)         or _global_forward_hooks or _global_forward_pre_hooks):
-> [1541](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1541)     return forward_call(*args, **kwargs)
   [1543](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1543) try:
   [1544](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1544)     result = None

File c:\Users\kkgg3\.conda\envs\tdl\Lib\site-packages\torch_geometric\nn\dense\linear.py:147, in Linear.forward(self, x)
    [141](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch_geometric/nn/dense/linear.py:141) def forward(self, x: Tensor) -> Tensor:
    [142](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch_geometric/nn/dense/linear.py:142)     r"""Forward pass.
    [143](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch_geometric/nn/dense/linear.py:143) 
    [144](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch_geometric/nn/dense/linear.py:144)     Args:
    [145](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch_geometric/nn/dense/linear.py:145)         x (torch.Tensor): The input features.
    [146](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch_geometric/nn/dense/linear.py:146)     """
--> [147](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch_geometric/nn/dense/linear.py:147)     return F.linear(x, self.weight, self.bias)

RuntimeError: mat1 and mat2 shapes cannot be multiplied (3x1 and 3x32)

as I attempted to fix issue by changing:

class PointGNN_Normalization(torch.nn.Module):
    def __init__(self, architecture_args = {
        'model_type': 'PointGNN_Normalization',
        'num_node_features': 3,
        'num_classes': 40,
        'projected_feature_channels': 128,
        'embedd_space': [3, 32, 64, 128],
        'n_layers': 2
    }):

to

class PointGNN_Normalization(torch.nn.Module):
    def __init__(self, architecture_args = {
        'model_type': 'PointGNN_Normalization',
        'num_node_features': 3,
        'num_classes': 40,
        'projected_feature_channels': 128,
        'embedd_space': [1, 3, 32, 64],
        'n_layers': 2
    }):

I got this error: 2.

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[1], [line 12](vscode-notebook-cell:?execution_count=1&line=12)
      [7](vscode-notebook-cell:?execution_count=1&line=7) data_list = [Data(x=torch.tensor([[-1], [0], [1]], dtype=torch.float), 
      [8](vscode-notebook-cell:?execution_count=1&line=8)             edge_index=torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]], dtype=torch.long))]
     [10](vscode-notebook-cell:?execution_count=1&line=10) batch = Batch.from_data_list(data_list)
---> [12](vscode-notebook-cell:?execution_count=1&line=12) out = model(batch)

File c:\Users\kkgg3\.conda\envs\tdl\Lib\site-packages\torch\nn\modules\module.py:1532, in Module._wrapped_call_impl(self, *args, **kwargs)
   [1530](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1530)     return self._compiled_call_impl(*args, **kwargs)  # type: ignore[misc]
   [1531](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1531) else:
-> [1532](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1532)     return self._call_impl(*args, **kwargs)

File c:\Users\kkgg3\.conda\envs\tdl\Lib\site-packages\torch\nn\modules\module.py:1541, in Module._call_impl(self, *args, **kwargs)
   [1536](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1536) # If we don't have any hooks, we want to skip the rest of the logic in
   [1537](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1537) # this function, and just call forward.
   [1538](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1538) if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
   [1539](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1539)         or _global_backward_pre_hooks or _global_backward_hooks
   [1540](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1540)         or _global_forward_hooks or _global_forward_pre_hooks):
-> [1541](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1541)     return forward_call(*args, **kwargs)
   [1543](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1543) try:
   [1544](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1544)     result = None

File c:\Users\kkgg3\point-gnn-pytorch\src\point_gnn_pytorch\models.py:147, in PointGNN_Normalization.forward(self, data)
    [144](file:///C:/Users/kkgg3/point-gnn-pytorch/src/point_gnn_pytorch/models.py:144) x = F.leaky_relu(x)
    [146](file:///C:/Users/kkgg3/point-gnn-pytorch/src/point_gnn_pytorch/models.py:146) # 2. apply various PointGNN convolutions 
--> [147](file:///C:/Users/kkgg3/point-gnn-pytorch/src/point_gnn_pytorch/models.py:147) x, pos, edge_index, edge_weight = self.convolutions((x, pos, edge_index, edge_weight))    
    [149](file:///C:/Users/kkgg3/point-gnn-pytorch/src/point_gnn_pytorch/models.py:149) # 3.  
    [150](file:///C:/Users/kkgg3/point-gnn-pytorch/src/point_gnn_pytorch/models.py:150) x = global_mean_pool(x, data.batch)

File c:\Users\kkgg3\.conda\envs\tdl\Lib\site-packages\torch\nn\modules\module.py:1532, in Module._wrapped_call_impl(self, *args, **kwargs)
   [1530](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1530)     return self._compiled_call_impl(*args, **kwargs)  # type: ignore[misc]
   [1531](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1531) else:
-> [1532](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1532)     return self._call_impl(*args, **kwargs)

File c:\Users\kkgg3\.conda\envs\tdl\Lib\site-packages\torch\nn\modules\module.py:1541, in Module._call_impl(self, *args, **kwargs)
   [1536](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1536) # If we don't have any hooks, we want to skip the rest of the logic in
   [1537](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1537) # this function, and just call forward.
   [1538](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1538) if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
   [1539](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1539)         or _global_backward_pre_hooks or _global_backward_hooks
   [1540](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1540)         or _global_forward_hooks or _global_forward_pre_hooks):
-> [1541](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1541)     return forward_call(*args, **kwargs)
   [1543](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1543) try:
   [1544](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1544)     result = None

File c:\Users\kkgg3\.conda\envs\tdl\Lib\site-packages\torch\nn\modules\container.py:217, in Sequential.forward(self, input)
    [215](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/container.py:215) def forward(self, input):
    [216](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/container.py:216)     for module in self:
--> [217](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/container.py:217)         input = module(input)
    [218](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/container.py:218)     return input

File c:\Users\kkgg3\.conda\envs\tdl\Lib\site-packages\torch\nn\modules\module.py:1532, in Module._wrapped_call_impl(self, *args, **kwargs)
   [1530](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1530)     return self._compiled_call_impl(*args, **kwargs)  # type: ignore[misc]
   [1531](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1531) else:
-> [1532](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1532)     return self._call_impl(*args, **kwargs)

File c:\Users\kkgg3\.conda\envs\tdl\Lib\site-packages\torch\nn\modules\module.py:1541, in Module._call_impl(self, *args, **kwargs)
   [1536](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1536) # If we don't have any hooks, we want to skip the rest of the logic in
   [1537](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1537) # this function, and just call forward.
   [1538](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1538) if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
   [1539](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1539)         or _global_backward_pre_hooks or _global_backward_hooks
   [1540](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1540)         or _global_forward_hooks or _global_forward_pre_hooks):
-> [1541](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1541)     return forward_call(*args, **kwargs)
   [1543](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1543) try:
   [1544](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1544)     result = None

File c:\Users\kkgg3\point-gnn-pytorch\src\point_gnn_pytorch\models.py:69, in ConvBlock.forward(self, arguments)
     [66](file:///C:/Users/kkgg3/point-gnn-pytorch/src/point_gnn_pytorch/models.py:66) def forward(self, arguments):
     [67](file:///C:/Users/kkgg3/point-gnn-pytorch/src/point_gnn_pytorch/models.py:67)     x, pos, edge_index, edge_weight = arguments
---> [69](file:///C:/Users/kkgg3/point-gnn-pytorch/src/point_gnn_pytorch/models.py:69)     x = self.conv(x, pos, edge_index, edge_weight)
     [71](file:///C:/Users/kkgg3/point-gnn-pytorch/src/point_gnn_pytorch/models.py:71)     x = self.bn(x)
     [73](file:///C:/Users/kkgg3/point-gnn-pytorch/src/point_gnn_pytorch/models.py:73)     x = self.activation(x)

File c:\Users\kkgg3\.conda\envs\tdl\Lib\site-packages\torch\nn\modules\module.py:1532, in Module._wrapped_call_impl(self, *args, **kwargs)
   [1530](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1530)     return self._compiled_call_impl(*args, **kwargs)  # type: ignore[misc]
   [1531](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1531) else:
-> [1532](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1532)     return self._call_impl(*args, **kwargs)

File c:\Users\kkgg3\.conda\envs\tdl\Lib\site-packages\torch\nn\modules\module.py:1541, in Module._call_impl(self, *args, **kwargs)
   [1536](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1536) # If we don't have any hooks, we want to skip the rest of the logic in
   [1537](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1537) # this function, and just call forward.
   [1538](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1538) if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
   [1539](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1539)         or _global_backward_pre_hooks or _global_backward_hooks
   [1540](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1540)         or _global_forward_hooks or _global_forward_pre_hooks):
-> [1541](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1541)     return forward_call(*args, **kwargs)
   [1543](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1543) try:
   [1544](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1544)     result = None

File c:\Users\kkgg3\point-gnn-pytorch\src\point_gnn_pytorch\models.py:31, in PointGNNConv_Edgeweight.forward(self, x, pos, edge_index, edge_weight)
     [29](file:///C:/Users/kkgg3/point-gnn-pytorch/src/point_gnn_pytorch/models.py:29) """"""
     [30](file:///C:/Users/kkgg3/point-gnn-pytorch/src/point_gnn_pytorch/models.py:30) # propagate_type: (x: Tensor, pos: Tensor)
---> [31](file:///C:/Users/kkgg3/point-gnn-pytorch/src/point_gnn_pytorch/models.py:31) out = self.propagate(edge_index, x=x, pos=pos, edge_weight=edge_weight, size=None)
     [32](file:///C:/Users/kkgg3/point-gnn-pytorch/src/point_gnn_pytorch/models.py:32) out = self.mlp_g(out)
     [33](file:///C:/Users/kkgg3/point-gnn-pytorch/src/point_gnn_pytorch/models.py:33) return x + out

TypeError: propagate() got an unexpected keyword argument 'edge_weight'

3. If I remove edge_weight=edge_weight from line 31 out = self.propagate(edge_index, x=x, pos=pos, edge_weight=edge_weight, size=None) in models.py

I get

---------------------------------------------------------------------------
UnboundLocalError                         Traceback (most recent call last)
Cell In[1], [line 12](vscode-notebook-cell:?execution_count=1&line=12)
      [7](vscode-notebook-cell:?execution_count=1&line=7) data_list = [Data(x=torch.tensor([[-1], [0], [1]], dtype=torch.float), 
      [8](vscode-notebook-cell:?execution_count=1&line=8)             edge_index=torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]], dtype=torch.long))]
     [10](vscode-notebook-cell:?execution_count=1&line=10) batch = Batch.from_data_list(data_list)
---> [12](vscode-notebook-cell:?execution_count=1&line=12) out = model(batch)

File c:\Users\kkgg3\.conda\envs\tdl\Lib\site-packages\torch\nn\modules\module.py:1532, in Module._wrapped_call_impl(self, *args, **kwargs)
   [1530](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1530)     return self._compiled_call_impl(*args, **kwargs)  # type: ignore[misc]
   [1531](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1531) else:
-> [1532](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1532)     return self._call_impl(*args, **kwargs)

File c:\Users\kkgg3\.conda\envs\tdl\Lib\site-packages\torch\nn\modules\module.py:1541, in Module._call_impl(self, *args, **kwargs)
   [1536](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1536) # If we don't have any hooks, we want to skip the rest of the logic in
   [1537](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1537) # this function, and just call forward.
   [1538](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1538) if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
   [1539](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1539)         or _global_backward_pre_hooks or _global_backward_hooks
   [1540](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1540)         or _global_forward_hooks or _global_forward_pre_hooks):
-> [1541](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1541)     return forward_call(*args, **kwargs)
   [1543](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1543) try:
   [1544](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1544)     result = None

File c:\Users\kkgg3\point-gnn-pytorch\src\point_gnn_pytorch\models.py:147, in PointGNN_Normalization.forward(self, data)
    [144](file:///C:/Users/kkgg3/point-gnn-pytorch/src/point_gnn_pytorch/models.py:144) x = F.leaky_relu(x)
    [146](file:///C:/Users/kkgg3/point-gnn-pytorch/src/point_gnn_pytorch/models.py:146) # 2. apply various PointGNN convolutions 
--> [147](file:///C:/Users/kkgg3/point-gnn-pytorch/src/point_gnn_pytorch/models.py:147) x, pos, edge_index, edge_weight = self.convolutions((x, pos, edge_index, edge_weight))    
    [149](file:///C:/Users/kkgg3/point-gnn-pytorch/src/point_gnn_pytorch/models.py:149) # 3.  
    [150](file:///C:/Users/kkgg3/point-gnn-pytorch/src/point_gnn_pytorch/models.py:150) x = global_mean_pool(x, data.batch)

File c:\Users\kkgg3\.conda\envs\tdl\Lib\site-packages\torch\nn\modules\module.py:1532, in Module._wrapped_call_impl(self, *args, **kwargs)
   [1530](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1530)     return self._compiled_call_impl(*args, **kwargs)  # type: ignore[misc]
   [1531](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1531) else:
-> [1532](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1532)     return self._call_impl(*args, **kwargs)

File c:\Users\kkgg3\.conda\envs\tdl\Lib\site-packages\torch\nn\modules\module.py:1541, in Module._call_impl(self, *args, **kwargs)
   [1536](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1536) # If we don't have any hooks, we want to skip the rest of the logic in
   [1537](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1537) # this function, and just call forward.
   [1538](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1538) if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
   [1539](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1539)         or _global_backward_pre_hooks or _global_backward_hooks
   [1540](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1540)         or _global_forward_hooks or _global_forward_pre_hooks):
-> [1541](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1541)     return forward_call(*args, **kwargs)
   [1543](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1543) try:
   [1544](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1544)     result = None

File c:\Users\kkgg3\.conda\envs\tdl\Lib\site-packages\torch\nn\modules\container.py:217, in Sequential.forward(self, input)
    [215](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/container.py:215) def forward(self, input):
    [216](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/container.py:216)     for module in self:
--> [217](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/container.py:217)         input = module(input)
    [218](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/container.py:218)     return input

File c:\Users\kkgg3\.conda\envs\tdl\Lib\site-packages\torch\nn\modules\module.py:1532, in Module._wrapped_call_impl(self, *args, **kwargs)
   [1530](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1530)     return self._compiled_call_impl(*args, **kwargs)  # type: ignore[misc]
   [1531](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1531) else:
-> [1532](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1532)     return self._call_impl(*args, **kwargs)

File c:\Users\kkgg3\.conda\envs\tdl\Lib\site-packages\torch\nn\modules\module.py:1541, in Module._call_impl(self, *args, **kwargs)
   [1536](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1536) # If we don't have any hooks, we want to skip the rest of the logic in
   [1537](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1537) # this function, and just call forward.
   [1538](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1538) if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
   [1539](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1539)         or _global_backward_pre_hooks or _global_backward_hooks
   [1540](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1540)         or _global_forward_hooks or _global_forward_pre_hooks):
-> [1541](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1541)     return forward_call(*args, **kwargs)
   [1543](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1543) try:
   [1544](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1544)     result = None

File c:\Users\kkgg3\point-gnn-pytorch\src\point_gnn_pytorch\models.py:69, in ConvBlock.forward(self, arguments)
     [66](file:///C:/Users/kkgg3/point-gnn-pytorch/src/point_gnn_pytorch/models.py:66) def forward(self, arguments):
     [67](file:///C:/Users/kkgg3/point-gnn-pytorch/src/point_gnn_pytorch/models.py:67)     x, pos, edge_index, edge_weight = arguments
---> [69](file:///C:/Users/kkgg3/point-gnn-pytorch/src/point_gnn_pytorch/models.py:69)     x = self.conv(x, pos, edge_index, edge_weight)
     [71](file:///C:/Users/kkgg3/point-gnn-pytorch/src/point_gnn_pytorch/models.py:71)     x = self.bn(x)
     [73](file:///C:/Users/kkgg3/point-gnn-pytorch/src/point_gnn_pytorch/models.py:73)     x = self.activation(x)

File c:\Users\kkgg3\.conda\envs\tdl\Lib\site-packages\torch\nn\modules\module.py:1532, in Module._wrapped_call_impl(self, *args, **kwargs)
   [1530](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1530)     return self._compiled_call_impl(*args, **kwargs)  # type: ignore[misc]
   [1531](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1531) else:
-> [1532](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1532)     return self._call_impl(*args, **kwargs)

File c:\Users\kkgg3\.conda\envs\tdl\Lib\site-packages\torch\nn\modules\module.py:1541, in Module._call_impl(self, *args, **kwargs)
   [1536](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1536) # If we don't have any hooks, we want to skip the rest of the logic in
   [1537](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1537) # this function, and just call forward.
   [1538](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1538) if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
   [1539](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1539)         or _global_backward_pre_hooks or _global_backward_hooks
   [1540](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1540)         or _global_forward_hooks or _global_forward_pre_hooks):
-> [1541](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1541)     return forward_call(*args, **kwargs)
   [1543](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1543) try:
   [1544](file:///C:/Users/kkgg3/.conda/envs/tdl/Lib/site-packages/torch/nn/modules/module.py:1544)     result = None

File c:\Users\kkgg3\point-gnn-pytorch\src\point_gnn_pytorch\models.py:31, in PointGNNConv_Edgeweight.forward(self, x, pos, edge_index, edge_weight)
     [29](file:///C:/Users/kkgg3/point-gnn-pytorch/src/point_gnn_pytorch/models.py:29) """"""
     [30](file:///C:/Users/kkgg3/point-gnn-pytorch/src/point_gnn_pytorch/models.py:30) # propagate_type: (x: Tensor, pos: Tensor)
---> [31](file:///C:/Users/kkgg3/point-gnn-pytorch/src/point_gnn_pytorch/models.py:31) out = self.propagate(edge_index, x=x, pos=pos, size=None)
     [32](file:///C:/Users/kkgg3/point-gnn-pytorch/src/point_gnn_pytorch/models.py:32) out = self.mlp_g(out)
     [33](file:///C:/Users/kkgg3/point-gnn-pytorch/src/point_gnn_pytorch/models.py:33) return x + out

File ~\AppData\Local\Temp\src.point_gnn_pytorch.models_PointGNNConv_Edgeweight_propagate_p87ip1xh.py:195, in propagate(self, edge_index, x, pos, size)
    [191](https://file+.vscode-resource.vscode-cdn.net/c%3A/Users/kkgg3/point-gnn-pytorch/~/AppData/Local/Temp/src.point_gnn_pytorch.models_PointGNNConv_Edgeweight_propagate_p87ip1xh.py:191)     raise NotImplementedError("'message_and_aggregate' not implemented")
    [193](https://file+.vscode-resource.vscode-cdn.net/c%3A/Users/kkgg3/point-gnn-pytorch/~/AppData/Local/Temp/src.point_gnn_pytorch.models_PointGNNConv_Edgeweight_propagate_p87ip1xh.py:193) else:
--> [195](https://file+.vscode-resource.vscode-cdn.net/c%3A/Users/kkgg3/point-gnn-pytorch/~/AppData/Local/Temp/src.point_gnn_pytorch.models_PointGNNConv_Edgeweight_propagate_p87ip1xh.py:195)     kwargs = self.collect(
    [196](https://file+.vscode-resource.vscode-cdn.net/c%3A/Users/kkgg3/point-gnn-pytorch/~/AppData/Local/Temp/src.point_gnn_pytorch.models_PointGNNConv_Edgeweight_propagate_p87ip1xh.py:196)         edge_index,
    [197](https://file+.vscode-resource.vscode-cdn.net/c%3A/Users/kkgg3/point-gnn-pytorch/~/AppData/Local/Temp/src.point_gnn_pytorch.models_PointGNNConv_Edgeweight_propagate_p87ip1xh.py:197)         x,
    [198](https://file+.vscode-resource.vscode-cdn.net/c%3A/Users/kkgg3/point-gnn-pytorch/~/AppData/Local/Temp/src.point_gnn_pytorch.models_PointGNNConv_Edgeweight_propagate_p87ip1xh.py:198)         pos,
    [199](https://file+.vscode-resource.vscode-cdn.net/c%3A/Users/kkgg3/point-gnn-pytorch/~/AppData/Local/Temp/src.point_gnn_pytorch.models_PointGNNConv_Edgeweight_propagate_p87ip1xh.py:199)         mutable_size,
    [200](https://file+.vscode-resource.vscode-cdn.net/c%3A/Users/kkgg3/point-gnn-pytorch/~/AppData/Local/Temp/src.point_gnn_pytorch.models_PointGNNConv_Edgeweight_propagate_p87ip1xh.py:200)     )
    [202](https://file+.vscode-resource.vscode-cdn.net/c%3A/Users/kkgg3/point-gnn-pytorch/~/AppData/Local/Temp/src.point_gnn_pytorch.models_PointGNNConv_Edgeweight_propagate_p87ip1xh.py:202)     # Begin Message Forward Pre Hook #######################################
    [203](https://file+.vscode-resource.vscode-cdn.net/c%3A/Users/kkgg3/point-gnn-pytorch/~/AppData/Local/Temp/src.point_gnn_pytorch.models_PointGNNConv_Edgeweight_propagate_p87ip1xh.py:203)     if not torch.jit.is_scripting() and not is_compiling():

File ~\AppData\Local\Temp\src.point_gnn_pytorch.models_PointGNNConv_Edgeweight_propagate_p87ip1xh.py:78, in collect(self, edge_index, x, pos, size)
     [76](https://file+.vscode-resource.vscode-cdn.net/c%3A/Users/kkgg3/point-gnn-pytorch/~/AppData/Local/Temp/src.point_gnn_pytorch.models_PointGNNConv_Edgeweight_propagate_p87ip1xh.py:76) else:
     [77](https://file+.vscode-resource.vscode-cdn.net/c%3A/Users/kkgg3/point-gnn-pytorch/~/AppData/Local/Temp/src.point_gnn_pytorch.models_PointGNNConv_Edgeweight_propagate_p87ip1xh.py:77)     raise NotImplementedError
---> [78](https://file+.vscode-resource.vscode-cdn.net/c%3A/Users/kkgg3/point-gnn-pytorch/~/AppData/Local/Temp/src.point_gnn_pytorch.models_PointGNNConv_Edgeweight_propagate_p87ip1xh.py:78) assert edge_weight is not None
     [80](https://file+.vscode-resource.vscode-cdn.net/c%3A/Users/kkgg3/point-gnn-pytorch/~/AppData/Local/Temp/src.point_gnn_pytorch.models_PointGNNConv_Edgeweight_propagate_p87ip1xh.py:80) # Collect user-defined arguments:
     [81](https://file+.vscode-resource.vscode-cdn.net/c%3A/Users/kkgg3/point-gnn-pytorch/~/AppData/Local/Temp/src.point_gnn_pytorch.models_PointGNNConv_Edgeweight_propagate_p87ip1xh.py:81) # (1) - Collect `pos_j`:
     [82](https://file+.vscode-resource.vscode-cdn.net/c%3A/Users/kkgg3/point-gnn-pytorch/~/AppData/Local/Temp/src.point_gnn_pytorch.models_PointGNNConv_Edgeweight_propagate_p87ip1xh.py:82) if isinstance(pos, (tuple, list)):

UnboundLocalError: cannot access local variable 'edge_weight' where it is not associated with a value

So far, I have looked at torch_geometric.nn/models.MLP and torch_geometric.nn/conv.MessagePassing.propagate(), but couldn't make it work.

Here are my list of packages in conda env:

# Name                    Version                   Build  Channel
accessible-pygments       0.0.4                    pypi_0    pypi
aiohttp                   3.9.5                    pypi_0    pypi
aiosignal                 1.3.1                    pypi_0    pypi
alabaster                 0.7.16                   pypi_0    pypi
anyio                     4.3.0                    pypi_0    pypi
argon2-cffi               23.1.0                   pypi_0    pypi
argon2-cffi-bindings      21.2.0                   pypi_0    pypi
arrow                     1.3.0                    pypi_0    pypi
asttokens                 2.4.1                    pypi_0    pypi
async-lru                 2.0.4                    pypi_0    pypi
attrs                     23.2.0                   pypi_0    pypi
babel                     2.14.0                   pypi_0    pypi
beautifulsoup4            4.12.3                   pypi_0    pypi
bleach                    6.1.0                    pypi_0    pypi
bzip2                     1.0.8                h2bbff1b_5
ca-certificates           2024.3.11            haa95532_0
celluloid                 0.2.0                    pypi_0    pypi
certifi                   2024.2.2                 pypi_0    pypi
cffi                      1.16.0                   pypi_0    pypi
cfgv                      3.4.0                    pypi_0    pypi
charset-normalizer        3.3.2                    pypi_0    pypi
colorama                  0.4.6                    pypi_0    pypi
comm                      0.2.2                    pypi_0    pypi
coverage                  7.5.0                    pypi_0    pypi
debugpy                   1.8.1                    pypi_0    pypi
decorator                 5.1.1                    pypi_0    pypi
defusedxml                0.7.1                    pypi_0    pypi
distlib                   0.3.8                    pypi_0    pypi
docutils                  0.21.2                   pypi_0    pypi
execnet                   2.1.1                    pypi_0    pypi
executing                 2.0.1                    pypi_0    pypi
expat                     2.6.2                hd77b12b_0
fastjsonschema            2.19.1                   pypi_0    pypi
filelock                  3.13.4                   pypi_0    pypi
fqdn                      1.5.1                    pypi_0    pypi
frozenlist                1.4.1                    pypi_0    pypi
fsspec                    2024.3.1                 pypi_0    pypi
gensim                    4.3.2                    pypi_0    pypi
gudhi                     3.9.0                    pypi_0    pypi
h11                       0.14.0                   pypi_0    pypi
httpcore                  1.0.5                    pypi_0    pypi
httpx                     0.27.0                   pypi_0    pypi
hypernetx                 1.2.5                    pypi_0    pypi
identify                  2.5.36                   pypi_0    pypi
idna                      3.7                      pypi_0    pypi
igraph                    0.11.4                   pypi_0    pypi
imagesize                 1.4.1                    pypi_0    pypi
iniconfig                 2.0.0                    pypi_0    pypi
intel-openmp              2021.4.0                 pypi_0    pypi
ipykernel                 6.29.4                   pypi_0    pypi
ipython                   8.23.0                   pypi_0    pypi
ipywidgets                8.1.2                    pypi_0    pypi
isoduration               20.11.0                  pypi_0    pypi
jedi                      0.19.1                   pypi_0    pypi
jinja2                    3.1.3                    pypi_0    pypi
json5                     0.9.25                   pypi_0    pypi
jsonpointer               2.4                      pypi_0    pypi
jsonschema                4.21.1                   pypi_0    pypi
jsonschema-specifications 2023.12.1                pypi_0    pypi
jupyter                   1.0.0                    pypi_0    pypi
jupyter-client            8.6.1                    pypi_0    pypi
jupyter-console           6.6.3                    pypi_0    pypi
jupyter-core              5.7.2                    pypi_0    pypi
jupyter-events            0.10.0                   pypi_0    pypi
jupyter-lsp               2.2.5                    pypi_0    pypi
jupyter-server            2.14.0                   pypi_0    pypi
jupyter-server-terminals  0.5.3                    pypi_0    pypi
jupyterlab                4.1.6                    pypi_0    pypi
jupyterlab-pygments       0.3.0                    pypi_0    pypi
jupyterlab-server         2.27.1                   pypi_0    pypi
jupyterlab-widgets        3.0.10                   pypi_0    pypi
karateclub                1.3.4                    pypi_0    pypi
levenshtein               0.25.1                   pypi_0    pypi
libffi                    3.4.4                hd77b12b_0
markupsafe                2.1.5                    pypi_0    pypi
matplotlib-inline         0.1.7                    pypi_0    pypi
mistune                   3.0.2                    pypi_0    pypi
mkl                       2021.4.0                 pypi_0    pypi
mpmath                    1.3.0                    pypi_0    pypi
multidict                 6.0.5                    pypi_0    pypi
mypy                      1.10.0                   pypi_0    pypi
mypy-extensions           1.0.0                    pypi_0    pypi
nbclient                  0.10.0                   pypi_0    pypi
nbconvert                 7.16.3                   pypi_0    pypi
nbformat                  5.10.4                   pypi_0    pypi
nbsphinx                  0.9.3                    pypi_0    pypi
nbsphinx-link             1.3.0                    pypi_0    pypi
nest-asyncio              1.6.0                    pypi_0    pypi
networkx                  2.8.8                    pypi_0    pypi
nodeenv                   1.8.0                    pypi_0    pypi
notebook                  7.1.3                    pypi_0    pypi
notebook-shim             0.2.4                    pypi_0    pypi
numpydoc                  1.7.0                    pypi_0    pypi
openssl                   3.0.13               h2bbff1b_0
overrides                 7.7.0                    pypi_0    pypi
packaging                 24.0                     pypi_0    pypi
pandas-stubs              2.2.1.240316             pypi_0    pypi
pandocfilters             1.5.1                    pypi_0    pypi
parso                     0.8.4                    pypi_0    pypi
pip                       23.3.1          py312haa95532_0
platformdirs              4.2.1                    pypi_0    pypi
pluggy                    1.5.0                    pypi_0    pypi
pre-commit                3.7.0                    pypi_0    pypi
prometheus-client         0.20.0                   pypi_0    pypi
prompt-toolkit            3.0.43                   pypi_0    pypi
psutil                    5.9.8                    pypi_0    pypi
pure-eval                 0.2.2                    pypi_0    pypi
pycparser                 2.22                     pypi_0    pypi
pydata-sphinx-theme       0.15.2                   pypi_0    pypi
pyg-nightly               2.6.0.dev20240424          pypi_0    pypi
pygments                  2.17.2                   pypi_0    pypi
pygsp                     0.5.1                    pypi_0    pypi
pytest                    8.1.1                    pypi_0    pypi
pytest-cov                5.0.0                    pypi_0    pypi
pytest-split              0.8.2                    pypi_0    pypi
pytest-xdist              3.5.0                    pypi_0    pypi
python                    3.12.3               h1d929f7_0
python-dateutil           2.9.0.post0              pypi_0    pypi
python-igraph             0.11.4                   pypi_0    pypi
python-json-logger        2.0.7                    pypi_0    pypi
python-levenshtein        0.25.1                   pypi_0    pypi
python-louvain            0.16                     pypi_0    pypi
pywin32                   306                      pypi_0    pypi
pywinpty                  2.0.13                   pypi_0    pypi
pyyaml                    6.0.1                    pypi_0    pypi
pyzmq                     26.0.2                   pypi_0    pypi
qtconsole                 5.5.1                    pypi_0    pypi
qtpy                      2.4.1                    pypi_0    pypi
rapidfuzz                 3.8.1                    pypi_0    pypi
referencing               0.35.0                   pypi_0    pypi
requests                  2.31.0                   pypi_0    pypi
rfc3339-validator         0.1.4                    pypi_0    pypi
rfc3986-validator         0.1.1                    pypi_0    pypi
rpds-py                   0.18.0                   pypi_0    pypi
ruff                      0.4.1                    pypi_0    pypi
send2trash                1.8.3                    pypi_0    pypi
setuptools                68.2.2          py312haa95532_0
six                       1.16.0                   pypi_0    pypi
smart-open                7.0.4                    pypi_0    pypi
sniffio                   1.3.1                    pypi_0    pypi
snowballstemmer           2.2.0                    pypi_0    pypi
soupsieve                 2.5                      pypi_0    pypi
spharapy                  1.1.2                    pypi_0    pypi
sphinx                    7.3.7                    pypi_0    pypi
sphinx-gallery            0.15.0                   pypi_0    pypi
sphinxcontrib-applehelp   1.0.8                    pypi_0    pypi
sphinxcontrib-devhelp     1.0.6                    pypi_0    pypi
sphinxcontrib-htmlhelp    2.0.5                    pypi_0    pypi
sphinxcontrib-jsmath      1.0.1                    pypi_0    pypi
sphinxcontrib-qthelp      1.0.7                    pypi_0    pypi
sphinxcontrib-serializinghtml 1.1.10                   pypi_0    pypi
sqlite                    3.41.2               h2bbff1b_0
stack-data                0.6.3                    pypi_0    pypi
sympy                     1.12                     pypi_0    pypi
tabulate                  0.9.0                    pypi_0    pypi
tbb                       2021.12.0                pypi_0    pypi
terminado                 0.18.1                   pypi_0    pypi
texttable                 1.7.0                    pypi_0    pypi
tinycss2                  1.3.0                    pypi_0    pypi
tk                        8.6.12               h2bbff1b_0
topoembedx                0.0.1                    pypi_0    pypi
topomodelx                0.0.1                    pypi_0    pypi
toponetx                  0.0.2                    pypi_0    pypi
torch                     2.3.0+cpu                pypi_0    pypi
torch-cluster             1.6.3                    pypi_0    pypi
torch-geometric           2.5.3                    pypi_0    pypi
torch-scatter             2.1.2                    pypi_0    pypi
torch-sparse              0.6.18                   pypi_0    pypi
torch-spline-conv         1.2.2                    pypi_0    pypi
tornado                   6.4                      pypi_0    pypi
tqdm                      4.66.2                   pypi_0    pypi
traitlets                 5.14.3                   pypi_0    pypi
trimesh                   4.3.1                    pypi_0    pypi
types-python-dateutil     2.9.0.20240316           pypi_0    pypi
types-pytz                2024.1.0.20240417          pypi_0    pypi
types-requests            2.31.0.20240406          pypi_0    pypi
typing-extensions         4.11.0                   pypi_0    pypi
tzdata                    2024a                h04d1e81_0
uri-template              1.3.0                    pypi_0    pypi
urllib3                   2.2.1                    pypi_0    pypi
vc                        14.2                 h21ff451_1
virtualenv                20.26.0                  pypi_0    pypi
vs2015_runtime            14.27.29016          h5e58377_2
wcwidth                   0.2.13                   pypi_0    pypi
webcolors                 1.13                     pypi_0    pypi
webencodings              0.5.1                    pypi_0    pypi
websocket-client          1.8.0                    pypi_0    pypi
wheel                     0.41.2          py312haa95532_0
widgetsnbextension        4.0.10                   pypi_0    pypi
wrapt                     1.16.0                   pypi_0    pypi
xz                        5.4.6                h8cc25b3_0
yarl                      1.9.4                    pypi_0    pypi
zlib                      1.2.13               h8cc25b3_0

Thanks for taking look into this.

[certum-ai]

Hey! Thx for reaching out :) Back in the days I contributed PointGNN to torch_geometric . That version is working and also integrated with the latest updates. Hope this helps!