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.
class_docstr_tok: ["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","."]
code: def forward(self, is_train, req, in_data, out_data, aux):
      x = in_data[0]
      y = np.maximum(x, np.zeros_like(x))
      self.assign(out_data[0], req[0], y)
  
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 1 for calling the ReluOp obj's underlying member method code with example usage: obj.forward(is_train, req, in_data, out_data, aux) without return types