class_docstr: A linear method encapsulation similar to PyG's
  
  Parameters
  ----------
  in_channels (int)
  out_channels (int)
  bias (int)
  weight_initializer (string): (glorot or zeros)
class_docstr_tok: ["A","linear","method","encapsulation","similar","to","PyG's","Parameters","--","--","--","--","--","in_channels","(","int",")","out_channels","(","int",")","bias","(","int",")","weight_initializer","(","string",")",":","(","glorot","or","zeros",")"]
code: def forward(self, x):
      """"""
      return F.linear(x, self.weight, self.bias)
  
imports: from torch_geometric.nn import inits, MessagePassing
  from torch_geometric.nn import radius_graph
  from .features import d_angle_emb, d_theta_phi_emb
  from torch_scatter import scatter
  from torch_sparse import matmul
  import torch
  from torch import nn
  from torch.nn import Embedding
  import torch.nn.functional
  import numpy
label_desc: Use image node_id 3 for calling the Linear obj's underlying member method code with example usage: obj.forward(x) and returns: F