class_docstr: ReLU activation on both interval and concrete data
class_docstr_tok: ["ReLU","activation","on","both","interval","and","concrete","data"]
docstr: Concrete pass through the ReLU function
  
  :param x: concrete input to the activation function.
  :return: concrete outputs from the ReLU.
docstr_tok: ["Concrete","pass","through","the","ReLU","function",":","param","x",":","concrete","input","to","the","activation","function",".",":","return",":","concrete","outputs","from","the","ReLU","."]
code: def concrete_forward(self, x: "torch.Tensor") -> "torch.Tensor":
      """
      Concrete pass through the ReLU function
  
      :param x: concrete input to the activation function.
      :return: concrete 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 4 for calling the PyTorchIntervalReLU obj's underlying member method code with example usage: obj.concrete_forward(x) and returns: self