class_docstr: Wrapper around pytorch's convolutional layer.
  We only add the bias to the zeroth element of the zonotope
class_docstr_tok: ["Wrapper","around","pytorch","'s","convolutional","layer",".","We","only","add","the","bias","to","the","zeroth","element","of","the","zonotope"]
docstr: Abstract forward pass through the convolutional layer
  
  :param x: input zonotope to the convolutional layer.
  :return x: zonotope after being pushed through the convolutional layer.
docstr_tok: ["Abstract","forward","pass","through","the","convolutional","layer",":","param","x",":","input","zonotope","to","the","convolutional","layer",".",":","return","x",":","zonotope","after","being","pushed","through","the","convolutional","layer","."]
code: def forward(self, x: "torch.Tensor") -> "torch.Tensor":
      """
      Abstract forward pass through the convolutional layer
  
      :param x: input zonotope to the convolutional layer.
      :return x: zonotope after being pushed through the convolutional layer.
      """
      x = self.conv(x)
      x = self.zonotope_add(x)
      return x
  
imports: from typing import Tuple, Union
  import numpy
  import torch
label_desc: Use image node_id 3 for calling the ZonoConv obj's underlying member method code with example usage: obj.forward(x) and returns: x