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 __init__(
      self, channels, use_conv, dims=2, out_channels=None, padding=1
  ):
      super().__init__()
      self.channels = channels
      self.out_channels = out_channels or channels
      self.use_conv = use_conv
      self.dims = dims
      stride = 2 if dims != 3 else (1, 2, 2)
      if use_conv:
          self.op = conv_nd(
              dims,
              self.channels,
              self.out_channels,
              3,
              stride=stride,
              padding=padding,
          )
      else:
          assert self.channels == self.out_channels
          self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
  
imports: from abc import abstractmethod
  import math
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from ldm.modules.diffusionmodules.util import checkpoint, conv_nd, linear, avg_pool_nd, zero_module, normalization, timestep_embedding
  from ldm.modules.attention import SpatialTransformer
  from ldm.util import exists
label_desc: Use image node_id 1 to create a new Downsample object from inherited base classes: nn with example: obj = Downsample(channels, use_conv, dims, out_channels, padding)