class_docstr: Change the channel number with a one by one convoluational layer.
  
  Args:
      in_channels (int): Number of input channels.
      out_channels (int): Number of output channels.
code: def __init__(self, in_channels, out_channels, **kwargs):
      super(ChannelReductionEncoder, self).__init__()
  
      self.layer = nn.Conv2D(
          in_channels,
          out_channels,
          kernel_size=1,
          stride=1,
          padding=0,
          weight_attr=nn.initializer.XavierNormal(),
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import paddle
  from paddle import ParamAttr
  import paddle.nn
  import paddle.nn.functional
label_desc: Use image node_id 1 to create a new ChannelReductionEncoder object from inherited base classes: nn with example: obj = ChannelReductionEncoder(in_channels, out_channels)