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","."]
code: def __init__(self, in_features: int, out_features: int):
      super().__init__()
  
      self.weight = torch.nn.Parameter(
          torch.normal(
              mean=torch.zeros(out_features, in_features),
              std=torch.ones(out_features, in_features),
          )
      )
      self.bias = torch.nn.Parameter(
          torch.normal(
              mean=torch.zeros(out_features),
              std=torch.ones(out_features),
          )
      )
  
imports: from typing import List, Union, Tuple, Optional
  import torch
  import numpy
label_desc: Use image node_id 1 to create a new PyTorchIntervalDense object from inherited base classes: torch.nn with example: obj = PyTorchIntervalDense(in_features, out_features)