code: def test_count(self):
      # Tests if counts are what we would expect from different batches
  
      input_dicts_and_lengths = [
          (
              {
                  SampleBatch.OBS: {
                      "a": np.array([[1], [2], [3]]),
                      "b": np.array([[0], [0], [1]]),
                      "c": np.array([[4], [5], [6]]),
                  }
              },
              3,
          ),
          (
              {
                  SampleBatch.OBS: {
                      "a": np.array([[1, 2, 3]]),
                      "b": np.array([[0, 0, 1]]),
                      "c": np.array([[4, 5, 6]]),
                  }
              },
              1,
          ),
          (
              {
                  SampleBatch.INFOS: {
                      "a": np.array([[1], [2], [3]]),
                      "b": np.array([[0], [0], [1]]),
                      "c": np.array([[4], [5], [6]]),
                  }
              },
              0,  # This should have a length of zero, since we can ignore INFO
          ),
          (
              {
                  "state_in_0": {
                      "a": [
                          [[1], [2], [3]],
                          [[1], [2], [3]],
                          [[1], [2], [3]],
                      ],
                      "b": [
                          [[1], [2], [3]],
                          [[1], [2], [3]],
                          [[1], [2], [3]],
                      ],
                      "c": [
                          [[1], [2], [3]],
                          [[1], [2], [3]],
                          [[1], [2], [3]],
                      ],
                  },
                  "state_out_0": {
                      "a": [
                          [[1], [2], [3]],
                          [[1], [2], [3]],
                          [[1], [2], [3]],
                      ],
                      "b": [
                          [[1], [2], [3]],
                          [[1], [2], [3]],
                          [[1], [2], [3]],
                      ],
                      "c": [
                          [[1], [2], [3]],
                          [[1], [2], [3]],
                          [[1], [2], [3]],
                      ],
                  },
                  SampleBatch.OBS: {
                      "a": np.array([1, 2, 3]),
                      "b": np.array([0, 0, 1]),
                      "c": np.array([4, 5, 6]),
                  },
              },
              3,  # This should have a length of three - we count from OBS
          ),
          (
              {
                  "state_in_0": {
                      "a": [
                          [[1], [2], [3]],
                          [[1], [2], [3]],
                          [[1], [2], [3]],
                      ],
                      "b": [
                          [[1], [2], [3]],
                          [[1], [2], [3]],
                          [[1], [2], [3]],
                      ],
                      "c": [
                          [[1], [2], [3]],
                          [[1], [2], [3]],
                          [[1], [2], [3]],
                      ],
                  },
                  "state_out_0": {
                      "a": [
                          [[1], [2], [3]],
                          [[1], [2], [3]],
                          [[1], [2], [3]],
                      ],
                      "b": [
                          [[1], [2], [3]],
                          [[1], [2], [3]],
                          [[1], [2], [3]],
                      ],
                      "c": [
                          [[1], [2], [3]],
                          [[1], [2], [3]],
                          [[1], [2], [3]],
                      ],
                  },
              },
              0,  # This should have a length of zero, we don't attempt to count
          ),
          (
              {
                  SampleBatch.OBS: {
                      "a": np.array([[1], [2], [3]]),
                      "b": np.array([[0], [0], [1]]),
                      "c": np.array([[4], [5], [6]]),
                  },
                  SampleBatch.SEQ_LENS: np.array([[1], [2], [3]]),
              },
              6,  # This should have a length of six, since we don't try to infer
              # from inputs but count by sequence lengths
          ),
          (
              {
                  SampleBatch.NEXT_OBS: {
                      "a": {"b": np.array([[1], [2], [3]])},
                      "c": np.array([[4], [5], [6]]),
                  },
              },
              3,  # Test if we properly support nesting
          ),
      ]
  
      for input_dict, length in input_dicts_and_lengths:
          self.assertEqual(
              attempt_count_timesteps(copy.deepcopy(input_dict)), length
          )
          s = SampleBatch(input_dict)
          self.assertEqual(s.count, length)
  
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 15 for calling the TestSampleBatch obj's underlying member method code with example usage: obj.test_count() without return types