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: Initializes the DecoderBlock
  
  Parameters
  ----------
  input_dim : int, optional, by default 16
  output_dim : int, optional, by default 8
  stride : int, optional, by default 1
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code: def __init__(
      self, input_dim: int = 16, output_dim: int = 8, stride: int = 1
  ):
      """
      Initializes the DecoderBlock
  
      Parameters
      ----------
      input_dim : int, optional, by default 16
      output_dim : int, optional, by default 8
      stride : int, optional, by default 1
      """
      super().__init__()
      self.block = nn.Sequential(
          Snake1d(input_dim),
          WNConvTranspose1d(
              input_dim,
              output_dim,
              kernel_size=2 * stride,
              stride=stride,
              padding=math.ceil(stride / 2),
          ),
          ResidualUnit(output_dim, dilation=1),
          ResidualUnit(output_dim, dilation=3),
          ResidualUnit(output_dim, dilation=9),
      )
  
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 1 to create a new DecoderBlock object from inherited base classes: nn with example: obj = DecoderBlock(input_dim, output_dim, stride)