code: def forward(self, x):
      # Input: B x T x C; Output: B x T
      x = self.conv1(x.transpose(1, 2)).transpose(1, 2)
      x = F.dropout(self.ln1(x), p=self.dropout, training=self.training)
      x = self.conv2(x.transpose(1, 2)).transpose(1, 2)
      x = F.dropout(self.ln2(x), p=self.dropout, training=self.training)
      return self.proj(x).squeeze(dim=2)
  
file_path: textlesslib/textless/vocoders/hifigan/generator.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn import Conv1d, ConvTranspose1d
  from torch.nn.utils import remove_weight_norm, weight_norm
  from .resblock import ResBlock, init_weights, LRELU_SLOPE
label_desc: Use image node_id 2 for calling the VariancePredictor obj's underlying member method code with example usage: obj.forward(x) and returns: self