class_docstr: ReLU activation on both interval and concrete data
class_docstr_tok: ["ReLU","activation","on","both","interval","and","concrete","data"]
docstr: Abstract pass through the ReLU function
  
  :param x: abstract input to the activation function.
  :return: abstract outputs from the ReLU.
docstr_tok: ["Abstract","pass","through","the","ReLU","function",":","param","x",":","abstract","input","to","the","activation","function",".",":","return",":","abstract","outputs","from","the","ReLU","."]
code: def forward(self, x: "torch.Tensor") -> "torch.Tensor":
      """
      Abstract pass through the ReLU function
  
      :param x: abstract input to the activation function.
      :return: abstract outputs from the ReLU.
      """
      return self.activation(x)
  
imports: from typing import List, Union, Tuple, Optional
  import torch
  import numpy
label_desc: Use image node_id 3 for calling the PyTorchIntervalReLU obj's underlying member method code with example usage: obj.forward(x) and returns: self