code: def input_sequence() -> Tuple[torch.Tensor, List]:
      # generates a realistic looking synthetic sequence tensor, i.e.
      # each sequence will have non-zero tokens at the beginning with
      # trailing zero tokens, including a max length token with a single
      # zero token at the end.  Example:
      # [
      #   [3, 5, 6, 0, 0, 0],
      #   [10, 11, 12, 13, 14, 0],   # max length sequence
      #   [32, 0, 0, 0, 0, 0]        # minimum length sequence
      # ]
      input_tensor = torch.zeros(
          [BATCH_SIZE, SEQ_SIZE], dtype=torch.int32
      ).to(DEVICE)
      sequence_lengths = np.random.randint(1, SEQ_SIZE, size=BATCH_SIZE)
      for i in range(input_tensor.shape[0]):
          input_tensor[i, : sequence_lengths[i]] = torch.tensor(
              np.random.randint(2, VOCAB_SIZE, size=sequence_lengths[i])
          )
  
      # emulate idx2str structure
      idx2str = ["<PAD>", "<UNK>"] + [
          str(i) for i in range(2, VOCAB_SIZE)
      ]
  
      return input_tensor, idx2str
  
imports: from typing import List, Tuple
  import numpy
  import pytest
  import torch
  import torchtext
  from ludwig.constants import ENCODER_OUTPUT, LAST_HIDDEN, LOGITS, SEQUENCE, TEXT, TYPE
  from ludwig.features.sequence_feature import _SequencePreprocessing, SequenceInputFeature, SequenceOutputFeature
  from ludwig.features.text_feature import TextInputFeature, TextOutputFeature
  from ludwig.schema.features.sequence_feature import SequenceInputFeatureConfig, SequenceOutputFeatureConfig
  from ludwig.schema.features.text_feature import ECDTextInputFeatureConfig, ECDTextOutputFeatureConfig
  from ludwig.utils.torch_utils import get_torch_device
  from tests.integration_tests.utils import ENCODERS, sequence_feature
label_desc: Use image node_id 1 for calling a global function with example usage: input_sequence() and returns: input_tensor,  idx2str