code: def forward(self, context, lens):
      if context.size()[0] > 1:
          context = self.run_padded_sequence(context, lens)
          # to B, D, T
          context = context.transpose(1, 2)
      else:
          for conv in self.convolutions:
              context = self.dropout(F.relu(conv(context)))
  
      if self.lstm_type != "":
          context = context.transpose(1, 2)
          self.bilstm.flatten_parameters()
          if lens is not None:
              context = self.run_unsorted_inputs(
                  self.bilstm, context, lens
              )
          else:
              context = self.bilstm(context)[0]
          context = context.transpose(1, 2)
  
      x_hat = context
      if self.use_linear:
          x_hat = self.dense(context.transpose(1, 2)).transpose(1, 2)
  
      return x_hat
  
imports: from typing import Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.utils import parametrize
  from TTS.tts.layers.delightful_tts.kernel_predictor import KernelPredictor
label_desc: Use image node_id 4 for calling the ConvLSTMLinear obj's underlying member method code with example usage: obj.forward(context, lens) and returns: x_hat