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.
docstr: Args:
      feat (Tensor): Image features with the shape of
          :math:`(N, C_{in}, H, W)`.
  
  Returns:
      Tensor: A tensor of shape :math:`(N, C_{out}, H, W)`.
docstr_tok: ["Args",":","feat","(","Tensor",")",":","Image","features","with","the","shape","of",":","math",":","`","(","N",",","C_","{","in","}",",","H",",","W",")","`",".","Returns",":","Tensor",":","A","tensor","of","shape",":","math",":","`","(","N",",","C_","{","out","}",",","H",",","W",")","`","."]
code: def forward(self, feat):
      """
      Args:
          feat (Tensor): Image features with the shape of
              :math:`(N, C_{in}, H, W)`.
  
      Returns:
          Tensor: A tensor of shape :math:`(N, C_{out}, H, W)`.
      """
      return self.layer(feat)
  
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 2 for calling the ChannelReductionEncoder obj's underlying member method code with example usage: obj.forward(feat) and returns: self