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 concrete input
  
  :param x: datapoint in the concrete domain
  :return: Flattened input preserving the batch dimension.
docstr_tok: ["Flattens","the","provided","concrete","input",":","param","x",":","datapoint","in","the","concrete","domain",":","return",":","Flattened","input","preserving","the","batch","dimension","."]
code: def concrete_forward(x: "torch.Tensor") -> "torch.Tensor":
      """
      Flattens the provided concrete input
  
      :param x: datapoint in the concrete domain
      :return: Flattened input preserving the batch dimension.
      """
      return torch.reshape(x, (x.shape[0], -1))
  
imports: from typing import List, Union, Tuple, Optional
  import torch
  import numpy
label_desc: Use image node_id 4 for calling the PyTorchIntervalFlatten obj's underlying member method code with example usage: obj.concrete_forward(x) and returns: torch