class_docstr: Performs input / target transformation before feeding the data to a GeneralizedRCNN
  model.
  
  The transformations it perform are:
      - input normalization (mean subtraction and std division)
      - input / target resizing to match min_size / max_size
  
  It returns a ImageList for the inputs, and a List[Dict[Tensor]] for the targets
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docstr: Implements `random.choice` via torch ops so it can be compiled with
  TorchScript. Remove if https://github.com/pytorch/pytorch/issues/25803
  is fixed.
docstr_tok: ["Implements","`","random.choice","`","via","torch","ops","so","it","can","be","compiled","with","TorchScript",".","Remove","if","https",":","\/\/github.com\/pytorch\/pytorch\/issues\/25803","is","fixed","."]
code: def torch_choice(self, k):
      # type: (List[int]) -> int
      """
      Implements `random.choice` via torch ops so it can be compiled with
      TorchScript. Remove if https://github.com/pytorch/pytorch/issues/25803
      is fixed.
      """
      index = int(torch.empty(1).uniform_(0.0, float(len(k))).item())
      return k[index]
  
imports: import math
  from typing import List, Tuple, Dict, Optional
  import torch
  from torch import nn, Tensor
  import torchvision
  from .image_list import ImageList
label_desc: Use image node_id 3 for calling the GeneralizedRCNNTransform obj's underlying member method code with example usage: obj.torch_choice(k) and returns: k