code: def test_VarLengthSequences(self) -> None:
      seq_id = 1
  
      # of form {sequence_id: (offsets, Tuple(Tensor, Tensor))}
      a_T = (
          torch.tensor(
              [[0, 1, 3], [2, 3, 7], [4, 5, 8], [2, 3, 1]]
          ).float(),
          torch.ones(4, 3),
      )
      b_T = (
          torch.tensor(
              [[1, 1, 3], [2, 2, 5], [3, 3, 1], [9, 10, 4], [5, 1, 7]]
          ).float(),
          torch.ones(5, 3),
      )
      a_in = {seq_id: (torch.tensor([0, 1]), a_T)}
      b_in = {seq_id: (torch.tensor([0, 4]), b_T)}
      vls1 = transforms.VarLengthSequences(
          keys=["a", "b"], sequence_id=seq_id
      )
      vls2 = transforms.VarLengthSequences(
          keys=["a", "b"],
          sequence_id=seq_id,
          to_keys=["a_to_key", "b_to_key"],
      )
      vls3 = transforms.VarLengthSequences(
          keys=["a", "b"],
          sequence_id=seq_id,
          to_keys=["a_to_key", "b_to_key"],
          to_keys_item_presence=[
              "a_to_key_item_presence",
              "b_to_key_item_presence",
          ],
      )
      o1 = vls1({"a": a_in, "b": b_in})
      o2 = vls2({"a": a_in, "b": b_in})
      o3 = vls3({"a": a_in, "b": b_in})
  
      self.assertSetEqual(
          set(o1.keys()),
          {"a", "b", "a_item_presence", "b_item_presence"},
      )
      self.assertSetEqual(
          set(o2.keys()),
          {
              "a",
              "b",
              "a_to_key",
              "b_to_key",
              "a_to_key_item_presence",
              "b_to_key_item_presence",
          },
      )
      self.assertSetEqual(
          set(o3.keys()),
          {
              "a",
              "b",
              "a_to_key",
              "b_to_key",
              "a_to_key_item_presence",
              "b_to_key_item_presence",
          },
      )
  
      # ensure input values are not changed if output keys are different
      self.assertEqual(o2["a"], a_in)
      self.assertEqual(o2["b"], b_in)
      self.assertEqual(o3["a"], a_in)
      self.assertEqual(o3["b"], b_in)
  
      # Testing assertion in the constructor
      with self.assertRaises(AssertionError):
          transforms.VarLengthSequences(
              keys=["a", "b"], sequence_id=1, to_keys=["to_a"]
          )
  
      # output shapes are correct
      self.assertTupleEqual(tuple(o1["a"][0].shape), (6, 3))
      self.assertTupleEqual(tuple(o1["b"][0].shape), (8, 3))
  
      # output values are correct
      expected_a = torch.tensor(
          [
              [
                  [0, 1, 3],
                  [0, 0, 0],
                  [0, 0, 0],
                  [2, 3, 7],
                  [4, 5, 8],
                  [2, 3, 1],
              ],
          ]
      ).float()
      expected_b = torch.tensor(
          [
              [
                  [1, 1, 3],
                  [2, 2, 5],
                  [3, 3, 1],
                  [9, 10, 4],
                  [5, 1, 7],
                  [0, 0, 0],
                  [0, 0, 0],
                  [0, 0, 0],
              ],
          ]
      ).float()
      self.assertEqual(o1["a"][0], expected_a)
      self.assertEqual(o1["b"][0], expected_b)
  
      # item presence tensors are correct
      extected_a_item_presence = torch.tensor(
          [[1, 0, 0], [1, 1, 1]]
      ).float()
      extected_b_item_presence = torch.tensor(
          [[1, 1, 1, 1], [1, 0, 0, 0]]
      ).float()
      self.assertEqual(o1["a_item_presence"], extected_a_item_presence)
      self.assertEqual(o1["b_item_presence"], extected_b_item_presence)
  
imports: import os
  import unittest
  from copy import deepcopy
  from typing import List
  from unittest.mock import Mock, patch
  import numpy
  import reagent.core.types
  import torch
  from reagent.preprocessing import transforms
  from reagent.preprocessing.types import InputColumn
label_desc: Use image node_id 29 for calling the TestTransforms obj's underlying member method code with example usage: obj.test_VarLengthSequences() without return types