class_docstr: An upsampling 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: ["An","upsampling","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 __init__(self, channels, use_conv, out_channels=None, factor=4):
      super().__init__()
      self.channels = channels
      self.out_channels = out_channels or channels
      self.use_conv = use_conv
      self.factor = factor
      if use_conv:
          ksize = 5
          pad = 2
          self.conv = nn.Conv1d(
              self.channels, self.out_channels, ksize, padding=pad
          )
  
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 1 to create a new Upsample object from inherited base classes: nn with example: obj = Upsample(channels, use_conv, out_channels, factor)