class_docstr: ReLU activation on both interval and concrete data
class_docstr_tok: ["ReLU","activation","on","both","interval","and","concrete","data"]
code: def __call__(self, x: "torch.Tensor") -> "torch.Tensor":
      return self.forward(x)
  
imports: from typing import List, Union, Tuple, Optional
  import torch
  import numpy
label_desc: Use image node_id 2 for calling the PyTorchIntervalReLU obj's underlying member method code with example usage: obj.__call__(x) and returns: self