class_docstr: Learned 2x upsampling without padding
class_docstr_tok: ["Learned","2x","upsampling","without","padding"]
code: def forward(self, x):
      return self.up(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 TransposedUpsample obj's underlying member method code with example usage: obj.forward(x) and returns: self