class_docstr: Class implementing a dense layer on a zonotope.
  Bias is only added to the zeroth term.
class_docstr_tok: ["Class","implementing","a","dense","layer","on","a","zonotope",".","Bias","is","only","added","to","the","zeroth","term","."]
docstr: Matrix multiplication for dense layer.
  
  :param x: input to the dense layer.
  :return: zonotope after weight multiplication.
docstr_tok: ["Matrix","multiplication","for","dense","layer",".",":","param","x",":","input","to","the","dense","layer",".",":","return",":","zonotope","after","weight","multiplication","."]
code: def zonotope_matmul(self, x: "torch.Tensor") -> "torch.Tensor":
      """
      Matrix multiplication for dense layer.
  
      :param x: input to the dense layer.
      :return: zonotope after weight multiplication.
      """
      return torch.matmul(x, torch.transpose(self.weight, 0, 1))
  
imports: from typing import Tuple, Union
  import numpy
  import torch
label_desc: Use image node_id 5 for calling the ZonoDenseLayer obj's underlying member method code with example usage: obj.zonotope_matmul(x) and returns: torch