docstr: features(Torch Tensor): size N x d d is the size of inputs
docstr_tok: ["features","(","Torch","Tensor",")",":","size","N","x","d","d","is","the","size","of","inputs"]
code: def forward(self, features):
      """
      features(Torch Tensor): size N x d d is the size of inputs
      """
      return torch.matmul(features, self.weight)
  
imports: import numpy
  import torch
  from torch import nn
  from torch.nn.parameter import Parameter
label_desc: Use image node_id 2 for calling the UnaryConv obj's underlying member method code with example usage: obj.forward(features) and returns: torch