code: def result_type_unary(
      a, dtype
  ):  # pylint: disable=missing-function-docstring
      """Find the result type from a single input and a dtype."""
      if dtype:
          # We need to let np_utils.result_type decide the dtype, not tf.zeros_like
          return result_type(dtype)
  
      # np_utils.result_type treats string inputs as dtype strings, not as strings.
      # but for unary we want to treat it as a string input.
      if isinstance(a, str):
          return np.unicode_
      elif isinstance(a, bytes):
          return np.bytes_
  
      # TF and numpy has different interpretations of Python types such as
      # `float`, so we let `np_utils.result_type` decide.
      return result_type(a)
  
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 26 for calling a global function with example usage: result_type_unary(a, dtype) and returns: result_type, result_type, np, np