class_docstr: A downsampling layer with an optional convolution.
  
  :param channels: channels in the inputs and outputs. :param use_conv: a bool determining if a convolution is
  applied. :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
               downsampling occurs in the inner-two dimensions.
class_docstr_tok: ["A","downsampling","layer","with","an","optional","convolution",".",":","param","channels",":","channels","in","the","inputs","and","outputs",".",":","param","use_conv",":","a","bool","determining","if","a","convolution","is","applied",".",":","param","dims",":","determines","if","the","signal","is","1D",",","2D",",","or","3D",".","If","3D",",","then","downsampling","occurs","in","the","inner-two","dimensions","."]
code: def forward(self, x):
      assert x.shape[1] == self.channels
      if self.use_conv and self.padding == 0:
          pad = (0, 1, 0, 1)
          x = pad_new(x, pad, mode="constant", value=0)
  
      assert x.shape[1] == self.channels
      x = self.conv(x)
  
      return x
  
imports: from functools import partial
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
label_desc: Use image node_id 2 for calling the Downsample2D obj's underlying member method code with example usage: obj.forward(x) and returns: x