code: def run_padded_sequence(self, context, lens):
      context_embedded = []
      for b_ind in range(context.size()[0]):  # TODO: speed up
          curr_context = context[
              b_ind : b_ind + 1, :, : lens[b_ind]
          ].clone()
          for conv in self.convolutions:
              curr_context = self.dropout(F.relu(conv(curr_context)))
          context_embedded.append(curr_context[0].transpose(0, 1))
      context = nn.utils.rnn.pad_sequence(
          context_embedded, batch_first=True
      )
      return context
  
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 2 for calling the ConvLSTMLinear obj's underlying member method code with example usage: obj.run_padded_sequence(context, lens) and returns: context