code: def forward(self, x, mask=None):
      # BxT -> BxCxT
      x = x.unsqueeze(1)
      if self.conv_type == "custom":
          for conv in self.conv_layers:
              if isinstance(conv, nn.LayerNorm):
                  x = x.transpose(1, 2)
                  x = conv(x).transpose(1, 2)
              else:
                  x = conv(x)
          x = x.transpose(2, 3).contiguous()
          x = x.view(x.size(0), -1, x.size(-1))
      else:
          for conv in self.conv_layers:
              x = conv(x)
          if self.conv_type == "conv2d":
              b, c, t, f = x.size()
              x = x.transpose(2, 3).contiguous().view(b, c * f, t)
      return x
  
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 2 for calling the ConvFeatureExtractionModel obj's underlying member method code with example usage: obj.forward(x, mask) and returns: x