code: def test_numpy_to_numpy():
      input_data = {"x": np.arange(12).reshape(3, 4)}
      expected_output = input_data
      actual_output = _convert_batch_type_to_numpy(input_data)
      assert expected_output == actual_output
  
      input_data = {
          "column_1": np.arange(12).reshape(3, 4),
          "column_2": np.arange(12).reshape(3, 4),
      }
      expected_output = {
          "column_1": np.arange(12).reshape(3, 4),
          "column_2": np.arange(12).reshape(3, 4),
      }
      actual_output = _convert_batch_type_to_numpy(input_data)
      assert input_data.keys() == expected_output.keys()
      np.testing.assert_array_equal(
          input_data["column_1"], expected_output["column_1"]
      )
      np.testing.assert_array_equal(
          input_data["column_2"], expected_output["column_2"]
      )
  
      input_data = np.arange(12).reshape(3, 4)
      expected_output = input_data
      actual_output = _convert_batch_type_to_numpy(input_data)
      np.testing.assert_array_equal(expected_output, actual_output)
  
imports: import pytest
  import warnings
  import pandas
  import numpy
  import pyarrow
  from ray.air._internal.torch_utils import convert_ndarray_to_torch_tensor
  from ray.air.constants import TENSOR_COLUMN_NAME
  from ray.air.util.data_batch_conversion import _convert_batch_type_to_pandas, _convert_pandas_to_batch_type, _convert_batch_type_to_numpy, _cast_ndarray_columns_to_tensor_extension, _cast_tensor_columns_to_ndarrays
  from ray.air.util.data_batch_conversion import BatchFormat
  from ray.air.util.tensor_extensions.pandas import TensorArray
  from ray.air.util.tensor_extensions.arrow import ArrowTensorArray
label_desc: Use image node_id 2 for calling a global function with example usage: test_numpy_to_numpy() without return types