code: def __init__(
      self, in_channels: int, out_channels: int, num_tags: int, **kwargs
  ):
      super().__init__()
  
      self.in_channels = in_channels
      self.out_channels = out_channels
  
      self.lins = torch.nn.ModuleList(
          [
              Linear(in_channels, out_channels, **kwargs)
              for _ in range(num_tags)
          ]
      )
  
      self.reset_parameters()
  
imports: from torch_cluster import radius_graph
  from torch_geometric.nn import GraphConv, GraphNorm
  from torch_geometric.nn import inits
  from torch_scatter import scatter, scatter_min
  from torch.nn import Embedding
  import torch
  from torch import nn
  from torch import Tensor
  import torch.nn.functional
  import math
  from math import sqrt
label_desc: Use image node_id 1 to create a new HeteroLinear object from inherited base classes: torch.nn with example: obj = HeteroLinear(in_channels, out_channels, num_tags)