code: def test_output_dtype(
      output_type, shape, dtype, order, out_dtype, mem_type
  ):
      with using_memory_type(mem_type):
          # Required assumptions for cudf outputs:
          if output_type in ("cudf", "dataframe", "series"):
              assume(dtype not in UNSUPPORTED_CUDF_DTYPES)
              assume(out_dtype not in UNSUPPORTED_CUDF_DTYPES)
          if output_type == "series":
              assume(not _multidimensional(shape))
  
          # Perform conversion
          inp = create_cuml_array_input("numpy", dtype, shape, order)
          ary = CumlArray(inp)
          res = ary.to_output(
              output_type=output_type, output_dtype=out_dtype
          )
  
          # Check output dtype
          if isinstance(res, (CudfDataFrame, PandasDataFrame)):
              res.values.dtype is out_dtype
          else:
              res.dtype is out_dtype
  
imports: import gc
  import operator
  import pickle
  import pytest
  from copy import deepcopy
  from cuml.internals.array import CumlArray, _order_to_strides, array_to_memory_order
  from cuml import global_settings
  from cuml.internals.mem_type import MemoryType
  from cuml.internals.memory_utils import _get_size_from_shape, determine_array_memtype, using_memory_type
  from cuml.internals.safe_imports import cpu_only_import, cpu_only_import_from, gpu_only_import, gpu_only_import_from
  from cuml.testing.strategies import UNSUPPORTED_CUDF_DTYPES, create_cuml_array_input, cuml_array_dtypes, cuml_array_input_types, cuml_array_inputs, cuml_array_orders, cuml_array_output_types, cuml_array_shapes, cuml_array_mem_types
  from cuml.testing.utils import normalized_shape, series_squeezed_shape, squeezed_shape, to_nparray
  from hypothesis import assume, given, settings
  from hypothesis import strategies
label_desc: Use image node_id 15 for calling a global function with example usage: test_output_dtype(output_type, shape, dtype, order, out_dtype, mem_type) without return types