class_docstr: Layer to handle flattening on both interval and concrete data
class_docstr_tok: ["Layer","to","handle","flattening","on","both","interval","and","concrete","data"]
code: def __init__(self):
      super().__init__()
  
imports: from typing import List, Union, Tuple, Optional
  import torch
  import numpy
label_desc: Use image node_id 1 to create a new PyTorchIntervalFlatten object from inherited base classes: torch.nn with example: obj = PyTorchIntervalFlatten()