class_docstr: ### Up-sampling layer
docstr: :param channels: is the number of channels
docstr_tok: [":","param","channels",":","is","the","number","of","channels"]
code: def __init__(self, channels: int):
      """
      :param channels: is the number of channels
      """
      super().__init__()
      # $3 \times 3$ convolution mapping
      self.conv = nn.Conv2d(channels, channels, 3, padding=1)
  
imports: import math
  from typing import List
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from labml_nn.diffusion.stable_diffusion.model.unet_attention import SpatialTransformer
label_desc: Use image node_id 1 to create a new UpSample object from inherited base classes: nn with example: obj = UpSample(channels)