class_docstr: Residual Block Type 2. It has 1 convolutional layers in each convolutional block.
  
  Network::
  
      x -> lrelu -> conv1-> -> z -> lrelu -> conv2-> o -> + -> o
      |---------------------------------------------------|
  
  
  Args:
      channels (int): number of hidden channels for the convolutional layers.
      kernel_size (int): size of the convolution filter in each layer.
      dilations (list): list of dilation value for each conv layer in a block.
class_docstr_tok: ["Residual","Block","Type","2",".","It","has","1","convolutional","layers","in","each","convolutional","block",".","Network",":",":","x","-",">","lrelu","-",">","conv1-",">","-",">","z","-",">","lrelu","-",">","conv2-",">","o","-",">","+","-",">","o","|","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","-|","Args",":","channels","(","int",")",":","number","of","hidden","channels","for","the","convolutional","layers",".","kernel_size","(","int",")",":","size","of","the","convolution","filter","in","each","layer",".","dilations","(","list",")",":","list","of","dilation","value","for","each","conv","layer","in","a","block","."]
code: def remove_weight_norm(self):
      for l in self.convs:
          remove_parametrizations(l, "weight")
  
imports: import torch
  from torch import nn
  from torch.nn import Conv1d, ConvTranspose1d
  from torch.nn import functional
  from torch.nn.utils.parametrizations import weight_norm
  from torch.nn.utils.parametrize import remove_parametrizations
  from TTS.utils.io import load_fsspec
label_desc: Use image node_id 3 for calling the ResBlock2 obj's underlying member method code with example usage: obj.remove_weight_norm() without return types