class_docstr: Wrapper on top of gluon.nn.Conv2D to capture the output and gradients of output of a Conv2D
  layer in a network. Use `set_capture_layer_name` to select the layer
  whose outputs and gradients of outputs need to be captured. After the backward pass,
  `conv_output` will contain the output and `conv_output.grad` will contain the
  output's gradients. Check gradcam_demo.py for example usage.
class_docstr_tok: ["Wrapper","on","top","of","gluon.nn.Conv2D","to","capture","the","output","and","gradients","of","output","of","a","Conv2D","layer","in","a","network",".","Use","`","set_capture_layer_name","`","to","select","the","layer","whose","outputs","and","gradients","of","outputs","need","to","be","captured",".","After","the","backward","pass",",","`","conv_output","`","will","contain","the","output","and","`","conv_output.grad","`","will","contain","the","output","'s","gradients",".","Check","gradcam_demo.py","for","example","usage","."]
code: def forward(self, x):
      out = self.conv(x)
      name = self._prefix[:-1]
      if name == Conv2D.capture_layer_name:
          out.attach_grad()
          Conv2D.conv_output = out
      return out
  
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 Conv2D obj's underlying member method code with example usage: obj.forward(x) and returns: out