code: def run_unsorted_inputs(
      self, fn, context, lens
  ):  # pylint: disable=no-self-use
      lens_sorted, ids_sorted = torch.sort(lens, descending=True)
      unsort_ids = [0] * lens.size(0)
      for i in range(
          len(ids_sorted)
      ):  # pylint: disable=consider-using-enumerate
          unsort_ids[ids_sorted[i]] = i
      lens_sorted = lens_sorted.long().cpu()
  
      context = context[ids_sorted]
      context = nn.utils.rnn.pack_padded_sequence(
          context, lens_sorted, batch_first=True
      )
      context = fn(context)[0]
      context = nn.utils.rnn.pad_packed_sequence(
          context, batch_first=True
      )[0]
  
      # map back to original indices
      context = context[unsort_ids]
      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 3 for calling the ConvLSTMLinear obj's underlying member method code with example usage: obj.run_unsorted_inputs(fn, context, lens) and returns: context