code: def promote_dtype(
      *args: Unpack[T], dtype=None, inexact=True
  ) -> tuple[Unpack[T]]:
      """ "Promotes input arguments to a specified or inferred dtype.
  
      All args are cast to the same dtype. See ``canonicalize_dtype`` for how
      this dtype is determined.
  
      The behavior of promote_dtype is mostly a convinience wrapper around
      ``jax.numpy.promote_types``. The differences being that it automatically casts
      all input to the inferred dtypes, allows inference to be overridden by a
      forced dtype, and has an optional check to garantuee the resulting dtype is
      inexact.
  
      Args:
        *args: JAX array compatible values. None values
          are returned as is.
        dtype: Optional dtype override. If specified the arguments are cast to
          the specified dtype instead and dtype inference is disabled.
        inexact: When True, the output dtype must be a subdtype
        of `jnp.inexact`. Inexact dtypes are real or complex floating points. This
        is useful when you want to apply operations that don't work directly on
        integers like taking a mean for example.
      Returns:
        The arguments cast to arrays of the same dtype.
      """
      dtype = canonicalize_dtype(*args, dtype=dtype, inexact=inexact)
      return tuple(
          jnp.asarray(x, dtype) if x is not None else None for x in args
      )
  
imports: from typing import Any, Optional
  from jax import numpy
  from typing_extensions import TypeVarTuple, Unpack
label_desc: Use image node_id 2 for calling a global function with example usage: promote_dtype() and returns: tuple