class_docstr: Class implementing a dense layer for the interval (box) domain.
class_docstr_tok: ["Class","implementing","a","dense","layer","for","the","interval","(","box",")","domain","."]
docstr: Performs the forward pass of the dense layer.
  
  :param x: concrete input to the convolutional layer.
  :return: output of the convolutional layer on x
docstr_tok: ["Performs","the","forward","pass","of","the","dense","layer",".",":","param","x",":","concrete","input","to","the","convolutional","layer",".",":","return",":","output","of","the","convolutional","layer","on","x"]
code: def concrete_forward(self, x: "torch.Tensor") -> "torch.Tensor":
      """
      Performs the forward pass of the dense layer.
  
      :param x: concrete input to the convolutional layer.
      :return: output of the convolutional layer on x
      """
      return (
          torch.matmul(x, torch.transpose(self.weight, 0, 1))
          + self.bias
      )
  
imports: from typing import List, Union, Tuple, Optional
  import torch
  import numpy
label_desc: Use image node_id 4 for calling the PyTorchIntervalDense obj's underlying member method code with example usage: obj.concrete_forward(x) and returns: unknown