code: def __init__(
      self,
      conv_layers: List[Tuple[int, int, int]],
      dropout: float = 0.0,
      mode: str = "default",
      conv_bias: bool = False,
      conv_type: str = "default",
  ):
      super().__init__()
  
      assert mode in {"default", "layer_norm"}
  
      def block(
          n_in,
          n_out,
          k,
          stride,
          is_layer_norm=False,
          is_group_norm=False,
          conv_bias=False,
      ):
          def make_conv():
              conv = nn.Conv1d(
                  n_in, n_out, k, stride=stride, bias=conv_bias
              )
              nn.init.kaiming_normal_(conv.weight)
              return conv
  
          assert (
              is_layer_norm and is_group_norm
          ) == False, "layer norm and group norm are exclusive"
  
          if is_layer_norm:
              return nn.Sequential(
                  make_conv(),
                  nn.Dropout(p=dropout),
                  nn.Sequential(
                      TransposeLast(),
                      Fp32LayerNorm(dim, elementwise_affine=True),
                      TransposeLast(),
                  ),
                  nn.GELU(),
              )
          elif is_group_norm:
              return nn.Sequential(
                  make_conv(),
                  nn.Dropout(p=dropout),
                  Fp32GroupNorm(dim, dim, affine=True),
                  nn.GELU(),
              )
          else:
              return nn.Sequential(
                  make_conv(), nn.Dropout(p=dropout), nn.GELU()
              )
  
      self.conv_type = conv_type
      if self.conv_type == "default":
          in_d = 1
          self.conv_layers = nn.ModuleList()
          for i, cl in enumerate(conv_layers):
              assert len(cl) == 3, "invalid conv definition: " + str(cl)
              (dim, k, stride) = cl
  
              self.conv_layers.append(
                  block(
                      in_d,
                      dim,
                      k,
                      stride,
                      is_layer_norm=mode == "layer_norm",
                      is_group_norm=mode == "default" and i == 0,
                      conv_bias=conv_bias,
                  )
              )
              in_d = dim
      elif self.conv_type == "conv2d":
          in_d = 1
          self.conv_layers = nn.ModuleList()
          for i, cl in enumerate(conv_layers):
              assert len(cl) == 3
              (dim, k, stride) = cl
  
              self.conv_layers.append(
                  torch.nn.Conv2d(in_d, dim, k, stride)
              )
              self.conv_layers.append(torch.nn.ReLU())
              in_d = dim
      elif self.conv_type == "custom":
          in_d = 1
          idim = 80
          self.conv_layers = nn.ModuleList()
          for i, cl in enumerate(conv_layers):
              assert len(cl) == 3
              (dim, k, stride) = cl
              self.conv_layers.append(
                  torch.nn.Conv2d(in_d, dim, k, stride, padding=1)
              )
              self.conv_layers.append(torch.nn.LayerNorm([dim, idim]))
              self.conv_layers.append(torch.nn.ReLU())
              in_d = dim
              if (i + 1) % 2 == 0:
                  self.conv_layers.append(
                      torch.nn.MaxPool2d(2, stride=2, ceil_mode=True)
                  )
                  idim = int(math.ceil(idim / 2))
      else:
          pass
  
imports: import logging
  import math
  from typing import List, Optional, Tuple
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn import LayerNorm
  from TTS.vc.modules.freevc.wavlm.modules import Fp32GroupNorm, Fp32LayerNorm, GLU_Linear, GradMultiply, MultiheadAttention, SamePad, TransposeLast, get_activation_fn, init_bert_params
label_desc: Use image node_id 1 to create a new ConvFeatureExtractionModel object from inherited base classes: nn with example: obj = ConvFeatureExtractionModel(conv_layers, dropout, mode, conv_bias, conv_type)