code: def result_type(*arrays_and_dtypes):
      """Returns the type resulting from applying NumPy type promotion to arguments.
  
      Args:
        *arrays_and_dtypes: A list of array_like objects or dtypes.
  
      Returns:
        A numpy dtype.
      """
  
      def maybe_get_dtype(x):
          # Don't put np.ndarray in this list, because np.result_type looks at the
          # value (not just dtype) of np.ndarray to decide the result type.
          if isinstance(
              x,
              (
                  arrays.ndarray,
                  arrays.ShardedNdArray,
                  tf.Tensor,
                  tf.IndexedSlices,
              ),
          ):
              return _to_numpy_type(x.dtype)
          elif isinstance(x, tf.DType):
              return _to_numpy_type(x)
          return x
  
      arrays_and_dtypes = [
          maybe_get_dtype(x) for x in tf.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 dtypes._result_type(*arrays_and_dtypes)
  
imports: import funcsigs
  import numpy
  import tensorflow.compat.v2
  from trax.tf_numpy.numpy_impl import arrays
  from trax.tf_numpy.numpy_impl import dtypes
label_desc: Use image node_id 7 for calling a global function with example usage: result_type() and returns: dtypes, x, _to_numpy_type, _to_numpy_type