class_docstr: An encoder block module for convolutional neural networks.
  
  This module constructs an encoder block consisting of a series of ResidualUnits and a final Snake1d
  activation followed by a weighted normalized 1D convolution. This block can be used as part of an
  encoder in architectures like autoencoders.
  
  Parameters
  ----------
  dim : int, optional
      The number of output channels. Default is 16.
  stride : int, optional
      The stride for the final convolutional layer. Default is 1.
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docstr: Initializes the EncoderBlock
  Parameters
  ----------
  dim : int, optional, by default 16
  stride : int, optional, by default 1
docstr_tok: ["Initializes","the","EncoderBlock","Parameters","--","--","--","--","--","dim",":","int",",","optional",",","by","default","16","stride",":","int",",","optional",",","by","default","1"]
code: def __init__(self, dim: int = 16, stride: int = 1):
      """
      Initializes the EncoderBlock
      Parameters
      ----------
      dim : int, optional, by default 16
      stride : int, optional, by default 1
      """
      super().__init__()
      self.block = nn.Sequential(
          ResidualUnit(dim // 2, dilation=1),
          ResidualUnit(dim // 2, dilation=3),
          ResidualUnit(dim // 2, dilation=9),
          Snake1d(dim // 2),
          WNConv1d(
              dim // 2,
              dim,
              kernel_size=2 * stride,
              stride=stride,
              padding=math.ceil(stride / 2),
          ),
      )
  
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 EncoderBlock object from inherited base classes: nn with example: obj = EncoderBlock(dim, stride)