code: def test_conversion(self):
      float_list_val = [1.1, 2.2, 3.3]
      tensor_val = torch.tensor([1.1, 2.2, 3.3], dtype=torch.double)
      array_val = np.array([1.1, 2.2, 3.3], dtype=np.float64)
      self.assertTrue(
          torch.equal(
              Values.to_tensor(float_list_val, dtype=torch.double),
              tensor_val,
          )
      )
      self.assertTrue(
          torch.equal(
              Values.to_tensor(tensor_val, dtype=torch.double),
              tensor_val,
          )
      )
      self.assertTrue(
          torch.equal(
              Values.to_tensor(array_val, dtype=torch.double),
              tensor_val,
          )
      )
      self.assertTrue(
          np.array_equal(Values.to_ndarray(float_list_val), array_val)
      )
      self.assertTrue(
          np.array_equal(
              Values.to_ndarray(tensor_val, dtype=np.float64), array_val
          )
      )
      self.assertTrue(
          np.array_equal(Values.to_ndarray(array_val), array_val)
      )
      self.assertEqual(
          Values.to_sequence(float_list_val), float_list_val
      )
      self.assertEqual(Values.to_sequence(tensor_val), float_list_val)
      self.assertEqual(Values.to_sequence(array_val), float_list_val)
  
imports: import unittest
  from typing import Tuple, Union
  import numpy
  import torch
  from reagent.ope.estimators.types import ActionDistribution, TypeWrapper, Values
label_desc: Use image node_id 6 for calling the TestValues obj's underlying member method code with example usage: obj.test_conversion() without return types