code: def result_type(
      *arrays_and_dtypes,
  ):  # pylint: disable=missing-function-docstring
      if ops.is_auto_dtype_conversion_enabled():
          # Use auto dtype conversion semantics for type inference.
          dtype, _ = flexible_dtypes.result_type(*arrays_and_dtypes)
          return dtype
      arrays_and_dtypes = [
          _maybe_get_dtype(x) for x in nest.flatten(arrays_and_dtypes)
      ]
      if not arrays_and_dtypes:
          # If arrays_and_dtypes is an empty list, let numpy decide what the dtype is.
          arrays_and_dtypes = [np.asarray([])]
      return np_dtypes._result_type(*arrays_and_dtypes)
  
imports: import inspect
  import numbers
  import os
  import re
  import numpy
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import flexible_dtypes
  from tensorflow.python.framework import indexed_slices
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import cond
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops.numpy_ops import np_arrays
  from tensorflow.python.ops.numpy_ops import np_dtypes
  from tensorflow.python.types import core
  from tensorflow.python.util import nest
  from tensorflow.python.util import tf_export
label_desc: Use image node_id 25 for calling a global function with example usage: result_type() and returns: np_dtypes, dtype