class_docstr: Modified ReLU as described in section 3.4 in https://arxiv.org/abs/1412.6806.
  This is used for guided backpropagation to get gradients of the image w.r.t activations.
  This Operator will do a regular backpropagation if `guided_backprop` is set to False
  and a guided packpropagation if `guided_backprop` is set to True. Check gradcam_demo.py
  for an example usage.
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code: def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
      if ReluOp.guided_backprop:
          # Get output and gradients of output
          y = out_data[0]
          dy = out_grad[0]
          # Zero out the negatives in the gradients of the output
          dy_positives = np.maximum(dy, np.zeros_like(dy))
          # What output values were greater than 0?
          y_ones = y.__gt__(0)
          # Mask out the values for which at least one of dy or y is negative
          dx = dy_positives * y_ones
          self.assign(in_grad[0], req[0], dx)
      else:
          # Regular backward for ReLU
          x = in_data[0]
          x_gt_zero = x.__gt__(0)
          dx = out_grad[0] * x_gt_zero
          self.assign(in_grad[0], req[0], dx)
  
imports: from __future__ import print_function
  import mxnet
  from mxnet import gluon, np, npx
  from mxnet import autograd
  from mxnet.gluon import nn
  import numpy
  import cv2
label_desc: Use image node_id 2 for calling the ReluOp obj's underlying member method code with example usage: obj.backward(req, out_grad, in_data, out_data, in_grad, aux) without return types