docstr: Tests whether to_device works properly under different circumstances
docstr_tok: ["Tests","whether","to_device","works","properly","under","different","circumstances"]
code: def test_to_device(self):
      """Tests whether to_device works properly under different circumstances"""
      torch, _ = try_import_torch()
  
      # sample batch includes
      #   a numpy array (a)
      #   a nested stucture of dict, tuple and lists (b) of numpys and None
      #   info dict
      #   a nested structure that ends up with tensors and ints(c)
      #   a tensor with float64 values (d)
      #   a float64 tensor with possibly wrong device (depends on if cuda available)
      #   repeated value object with np.array leaves (f)
  
      cuda_available = int(os.environ.get("RLLIB_NUM_GPUS", "0")) > 0
      cuda_if_possible = torch.device(
          "cuda:0" if cuda_available else "cpu"
      )
      s = SampleBatch(
          {
              "a": np.array([1, 2]),
              "b": {"c": (np.array([4, 5]), np.array([5, 6]))},
              "c": {
                  "d": torch.Tensor([1, 2]),
                  "g": (torch.Tensor([3, 4]), 1),
              },
              "d": torch.Tensor([1.0, 2.0]).double(),
              "e": torch.Tensor([1.0, 2.0])
              .double()
              .to(cuda_if_possible),
              "f": RepeatedValues(
                  np.array([[1, 2, 0, 0]]), lengths=[2], max_len=4
              ),
              SampleBatch.SEQ_LENS: np.array([2, 3, 1]),
              "state_in_0": np.array([1.0, 3.0, 4.0]),
              # INFO can have arbitrary elements, others need to conform in size
              SampleBatch.INFOS: np.array(
                  [{"a": 1}, {"b": [1, 2]}, {"c": None}]
              ),
          }
      )
  
      # inplace operation for sample_batch
      s.to_device(cuda_if_possible, framework="torch")
  
      def _check_recursive_device_and_type(input_struct, target_device):
          def get_mismatched_types(v):
              if isinstance(v, torch.Tensor):
                  if v.device.type != target_device.type:
                      return (v.device, v.dtype)
                  if v.is_floating_point() and v.dtype != torch.float32:
                      return (v.device, v.dtype)
  
          tree_checks = {}
          for k, v in input_struct.items():
              tree_checks[k] = tree.map_structure(
                  get_mismatched_types, v
              )
  
          self.assertTrue(
              all(v is None for v in tree.flatten((tree_checks))),
              f"the device type check dict: {tree_checks}",
          )
  
      # check if all tensors have the correct device and dtype
      _check_recursive_device_and_type(s, cuda_if_possible)
  
      # check repeated value
      check(s["f"].lengths, [2])
      check(s["f"].max_len, 4)
      check(s["f"].values, torch.from_numpy(np.asarray([[1, 2, 0, 0]])))
  
      # check infos
      check(
          s[SampleBatch.INFOS],
          np.array([{"a": 1}, {"b": [1, 2]}, {"c": None}]),
      )
  
      # check c/g/1
      self.assertEqual(s["c"]["g"][1], torch.from_numpy(np.asarray(1)))
  
      with self.assertRaises(NotImplementedError):
          # should raise an error if framework is not torch
          s.to_device(cuda_if_possible, framework="tf")
  
imports: import copy
  import functools
  import os
  import unittest
  import numpy
  import torch
  import tree
  import ray
  from ray.rllib.models.repeated_values import RepeatedValues
  from ray.rllib.policy.sample_batch import SampleBatch, attempt_count_timesteps, concat_samples
  from ray.rllib.utils.compression import is_compressed
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.test_utils import check
  from ray.rllib.utils.torch_utils import convert_to_torch_tensor
label_desc: Use image node_id 14 for calling the TestSampleBatch obj's underlying member method code with example usage: obj.test_to_device() and returns: v,  v, v,  v