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.
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","."]
code: def forward(self, x):
      assert x.shape[1] == self.channels
      return self.op(x)
  
imports: import functools
  import math
  import os
  import fsspec
  import torch
  import torch.nn
  import torch.nn.functional
  import torchaudio
  from transformers import LogitsWarper
  from TTS.tts.layers.tortoise.xtransformers import ContinuousTransformerWrapper, RelativePositionBias
label_desc: Use image node_id 2 for calling the Downsample obj's underlying member method code with example usage: obj.forward(x) and returns: self