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.
code: def __init__(self, channels, use_conv, dims=2, out_channels=None):
      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=1,
          )
      else:
          assert self.channels == self.out_channels
          self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
  
imports: import math
  from abc import abstractmethod
  import torch
  import torch.nn
  import torch.nn.functional
  from .fp16_util import convert_module_to_f16, convert_module_to_f32
  from .nn import avg_pool_nd, conv_nd, linear, normalization, timestep_embedding, zero_module
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)