class_docstr: A PyTorch module representing a block within the Decoder architecture.
  
  Parameters
  ----------
  input_dim : int, optional
      The number of input channels. Default is 16.
  output_dim : int, optional
      The number of output channels. Default is 8.
  stride : int, optional
      The stride for the transposed convolution, controlling the upsampling. Default is 1.
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docstr: Parameters
  ----------
  x : torch.tensor
  
  Returns
  -------
  torch.tensor
docstr_tok: ["Parameters","--","--","--","--","--","x",":","torch.tensor","Returns","--","--","--","-","torch.tensor"]
code: def forward(self, x):
      """
  
      Parameters
      ----------
      x : torch.tensor
  
      Returns
      -------
      torch.tensor
      """
      return self.block(x)
  
imports: import math
  from pathlib import Path
  from typing import List, Union
  import logging
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.utils.parametrizations import weight_norm
label_desc: Use image node_id 2 for calling the DecoderBlock obj's underlying member method code with example usage: obj.forward(x) and returns: self