code: def setUp(self) -> None:
      self.sorted_features = [1, 2, 5, 4]
      self.str_keyed_sparse_data = [
          {},
          {"2": 0.3},
          {"4": 0.5, "5": 0.4},
          {"5": 0.3, "1": 0.5, "2": 0.1, "4": 0.7},
      ]
      self.int_keyed_sparse_data = [
          {int(k): v for k, v in d.items()}
          for d in self.str_keyed_sparse_data
      ]
      self.expected_value_0 = torch.tensor(
          [
              [0, 0, 0, 0],
              [0, 0.3, 0, 0],
              [0, 0, 0.4, 0.5],
              [0.5, 0.1, 0.3, 0.7],
          ]
      )
      self.expected_presence_0 = torch.ones(4, 4).bool()
      MISSING = normalization.MISSING_VALUE
      self.expected_value_missing = torch.tensor(
          [
              [MISSING, MISSING, MISSING, MISSING],
              [MISSING, 0.3, MISSING, MISSING],
              [MISSING, MISSING, 0.4, 0.5],
              [0.5, 0.1, 0.3, 0.7],
          ]
      )
      self.expected_presence_missing = torch.tensor(
          [
              [False, False, False, False],
              [False, True, False, False],
              [False, False, True, True],
              [True, True, True, True],
          ]
      )
  
file_path: ReAgent/reagent/test/preprocessing/test_sparse_to_dense.py
imports: import unittest
  import torch
  from reagent.preprocessing import normalization
  from reagent.preprocessing.sparse_to_dense import PythonSparseToDenseProcessor, StringKeySparseToDenseProcessor
label_desc: Use image node_id 1 for calling the TestSparseToDense obj's underlying member method code with example usage: obj.setUp() without return types