class_docstr: Layer to pad and convolve input
      
class_docstr_tok: ["Layer","to","pad","and","convolve","input"]
code: def __init__(self, in_channels, out_channels, use_refl=True):
      super(Conv3x3, self).__init__()
      self.use_refl = use_refl
  
      with self.name_scope():
          self.pad = nn.ReflectionPad2D(1)
          self.conv = nn.Conv2D(
              in_channels=int(in_channels),
              channels=int(out_channels),
              kernel_size=3,
          )
  
imports: from __future__ import absolute_import, division, print_function
  import numpy
  import mxnet
  from mxnet.gluon import nn
label_desc: Use image node_id 1 to create a new Conv3x3 object from inherited base classes: nn with example: obj = Conv3x3(in_channels, out_channels, use_refl)