class_docstr: Randomly crop a torch tensor
  
  :param size: (tuple or list):
      dimensions of the crop
code: def __call__(self, *inputs):
      h_idx = random.randint(0, inputs[0].size(1) - self.size[0])
      w_idx = random.randint(0, inputs[1].size(2) - self.size[1])
      outputs = []
      idx = None
      for idx, _input in enumerate(inputs):
          _input = _input[
              :,
              h_idx : (h_idx + self.size[0]),
              w_idx : (w_idx + self.size[1]),
          ]
          outputs.append(_input)
      return outputs if idx >= 1 else outputs[0]
  
imports: import os
  import random
  import math
  import numpy
  import torch
label_desc: Use image node_id 2 for calling the RandomCrop obj's underlying member method code with example usage: obj.__call__() and returns: unknown