code: def cuml_array_inputs(
      draw,
      input_types=cuml_array_input_types(),
      dtypes=cuml_array_dtypes(),
      shapes=cuml_array_shapes(),
      orders=cuml_array_orders(),
  ):
      """
      Generates valid inputs for cuml arrays.
  
      Parameters
      ----------
      input_types: SearchStrategy[("numpy", "cupy", "series")], \
          default=cuml_array_input_tyes()
          A search strategy for the type of array input.
      dtypes: SearchStrategy[np.dtype], default=cuml_array_dtypes()
          A search strategy for a numpy/cupy compatible data type.
      shapes: SearchStrategy[int | tuple[int]], default=cuml_array_shapes()
          A search strategy for array shapes.
      orders : str in {'C', 'F'}, default=cuml_array_orders()
          A search strategy for array orders.
  
      Returns
      -------
      A strategy for valid cuml array inputs.
      """
      input_type = draw(input_types)
      dtype = draw(dtypes)
      shape = draw(shapes)
      order = draw(orders)
      multidimensional = isinstance(shape, tuple) and len(shape) > 1
      assume(
          not (
              input_type == "series"
              and (dtype in UNSUPPORTED_CUDF_DTYPES or multidimensional)
          )
      )
  
      data = draw(arrays(dtype=dtype, shape=shape))
  
      if input_type == "numpy":
          ret = np.asarray(data, order=order)
  
      elif input_type == "cupy":
          ret = cp.array(data, dtype=dtype, order=order)
  
      elif input_type == "series":
          ret = cudf.Series(data)
  
      else:
          raise ValueError(
              "The value for 'input_type' must be "
              f"one of {', '.join(_CUML_ARRAY_INPUT_TYPES)}."
          )
  
      # Cupy currently does not support masked arrays.
      cai = getattr(ret, "__cuda_array_interface__", dict())
      assume(cai.get("mask") is None)
  
      return ret
  
imports: from cuml.internals.array import CumlArray
  from cuml.internals.safe_imports import cpu_only_import, gpu_only_import
  from hypothesis import assume
  from hypothesis.extra.numpy import array_shapes, arrays, floating_dtypes, integer_dtypes
  from hypothesis.strategies import composite, integers, just, none, one_of, sampled_from
  from sklearn.datasets import make_classification, make_regression
  from sklearn.model_selection import train_test_split
label_desc: Use image node_id 9 for calling a global function with example usage: cuml_array_inputs(draw, input_types, dtypes, shapes, orders) and returns: ret