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.
  :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
               upsampling occurs in the inner-two dimensions.
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",".",":","param","dims",":","determines","if","the","signal","is","1D",",","2D",",","or","3D",".","If","3D",",","then","upsampling","occurs","in","the","inner-two","dimensions","."]
code: def forward(self, x):
      assert x.shape[1] == self.channels
      if self.dims == 3:
          x = F.interpolate(
              x,
              (x.shape[2], x.shape[3] * 2, x.shape[4] * 2),
              mode="nearest",
          )
      else:
          x = F.interpolate(x, scale_factor=2, mode="nearest")
      if self.use_conv:
          x = self.conv(x)
      return x
  
imports: from abc import abstractmethod
  from functools import partial
  import math
  from typing import Iterable
  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
label_desc: Use image node_id 2 for calling the Upsample obj's underlying member method code with example usage: obj.forward(x) and returns: x