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 __init__(
      self,
      channels,
      kernel_size,
      strides=(1, 1),
      padding=(0, 0),
      dilation=(1, 1),
      groups=1,
      layout="NCHW",
      activation=None,
      use_bias=True,
      weight_initializer=None,
      bias_initializer="zeros",
      in_channels=0,
      **kwargs,
  ):
      super(Conv2D, self).__init__(**kwargs)
      self.conv = nn.Conv2D(
          channels,
          kernel_size,
          strides=strides,
          padding=padding,
          dilation=dilation,
          groups=groups,
          layout=layout,
          activation=activation,
          use_bias=use_bias,
          weight_initializer=weight_initializer,
          bias_initializer=bias_initializer,
          in_channels=in_channels,
      )
  
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 to create a new Conv2D object from inherited base classes: mx.gluon with example: obj = Conv2D(channels, kernel_size, strides, padding, dilation, groups, layout, activation, use_bias, weight_initializer, bias_initializer, in_channels)