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.
class_docstr_tok: ["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","."]
docstr: Parameters
  ----------
  x : torch.tensor
  
  Returns
  -------
  torch.tensor
docstr_tok: ["Parameters","--","--","--","--","--","x",":","torch.tensor","Returns","--","--","--","-","torch.tensor"]
code: def forward(self, x: torch.tensor):
      """
      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 EncoderBlock obj's underlying member method code with example usage: obj.forward(x) and returns: self