code: def __init__(
      self,
      in_channels,
      out_channels,
      bias=True,
      weight_initializer="glorot",
      bias_initializer="zeros",
  ):
      super().__init__()
      self.in_channels = in_channels
      self.out_channels = out_channels
      self.weight_initializer = weight_initializer
      self.bias_initializer = bias_initializer
  
      assert in_channels > 0
      self.weight = nn.Parameter(
          torch.Tensor(out_channels, in_channels)
      )
  
      if bias:
          self.bias = nn.Parameter(torch.Tensor(out_channels))
      else:
          self.register_parameter("bias", None)
  
      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 Linear object from inherited base classes: torch.nn with example: obj = Linear(in_channels, out_channels, bias, weight_initializer, bias_initializer)