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"]
docstr: Flattens the provided abstract input
  
  :param x: datapoint in the interval domain
  :return: Flattened input preserving the batch and bounds dimensions.
docstr_tok: ["Flattens","the","provided","abstract","input",":","param","x",":","datapoint","in","the","interval","domain",":","return",":","Flattened","input","preserving","the","batch","and","bounds","dimensions","."]
code: def forward(x: "torch.Tensor") -> "torch.Tensor":
      """
      Flattens the provided abstract input
  
      :param x: datapoint in the interval domain
      :return: Flattened input preserving the batch and bounds dimensions.
      """
      return torch.reshape(x, (x.shape[0], 2, -1))
  
imports: from typing import List, Union, Tuple, Optional
  import torch
  import numpy
label_desc: Use image node_id 3 for calling the PyTorchIntervalFlatten obj's underlying member method code with example usage: obj.forward(x) and returns: torch