code: def forward(self, x):
      h, w, _ = x.shape
      th, tw = (self._size, self._size)
      if w == tw and h == th:
          return 0, 0, h, w
  
      i = random.randint(0, h - th)
      j = random.randint(0, w - tw)
      out = x[i : i + th, j : j + tw, :]
      return out
  
imports: import argparse
  import random
  from mxnet import gluon, image, autograd
  from mxnet.gluon.data.vision import transforms
  from mxnet.base import numeric_types
  import gluoncv
  from gluoncv import utils
  from mxnet.gluon.data import DataLoader
  from mxnet.gluon import nn
  import mxnet.ndarray
  import mxnet
  import os
  import numpy
  from mxboard import SummaryWriter
label_desc: Use image node_id 2 for calling the RandomCrop obj's underlying member method code with example usage: obj.forward(x) and returns: out, int,  int,  h,  w