code: def _call_tf_impl(*args_jax_flat, callable_flat_tf, **_):
      # On GPU we use dlpack to avoid copies of data to the host.
      def _arg_jax_to_tf(arg_jax):
          if (
              isinstance(arg_jax, jax.Array)
              and list(arg_jax.devices())[0].platform
              in _DLPACK_PLATFORMS
              and arg_jax.dtype in dlpack.SUPPORTED_DTYPES
          ):
              arg_dlpack = jax.dlpack.to_dlpack(
                  arg_jax, take_ownership=False
              )
              return tf.experimental.dlpack.from_dlpack(arg_dlpack)
          # The following avoids copies to the host on CPU, always for Array
          # and even for ndarray if they are sufficiently aligned.
          # TODO(necula): on TPU this copies to the host!
          if getattr(arg_jax, "dtype", None) == dtypes.float0:
              return tf.zeros(
                  shape=arg_jax.shape,
                  dtype=jax2tf_internal._tf_np_dtype_for_float0,
              )
          return tf.constant(np.asarray(arg_jax))
  
      args_tf_flat = tuple(map(_arg_jax_to_tf, args_jax_flat))
      with jax2tf_internal.inside_call_tf():
          # Call in TF eager mode
          res_tf_flat = callable_flat_tf(*args_tf_flat)
  
      def _res_tf_to_jax(res_tf: TfVal):
          res_tf, _ = jax2tf_internal._tfval_to_tensor_jax_dtype(res_tf)
          if (
              isinstance(res_tf, tf.Tensor)
              and res_tf.dtype in dlpack.SUPPORTED_DTYPES
          ):
              res_tf_platform = tf.DeviceSpec.from_string(
                  res_tf.backing_device
              ).device_type
              res_jax_platform = res_tf_platform.lower()
              if res_jax_platform in _DLPACK_PLATFORMS:
                  res_dlpack = tf.experimental.dlpack.to_dlpack(res_tf)
                  return jax.dlpack.from_dlpack(res_dlpack)
  
          # When working with a bfloat16 scalar tf.Tensor,np.asarray() can fail.
          # To handle this special case, we create a numpy copy.
          if (
              res_tf.shape == tf.TensorShape([])
              and res_tf.dtype == tf.bfloat16
          ):
              return jax.device_put(jnp.array(res_tf.numpy()))
          else:
              return jax.device_put(np.asarray(res_tf))
  
      return list(map(_res_tf_to_jax, res_tf_flat))
  
imports: from __future__ import annotations
  from collections.abc import Sequence
  import dataclasses
  import functools
  from typing import Any, Callable, Optional
  from absl import logging
  import jax
  from jax import dlpack
  from jax import dtypes
  from jax import numpy
  from jax import tree_util
  from jax._src import ad_util
  from jax._src import core
  from jax._src import effects
  from jax._src import util
  from jax._src.lib import xla_client
  from jax._src.lib.mlir import ir
  from jax._src.lib.mlir.dialects import func
  from jax._src.lib.mlir.dialects import hlo
  from jax.experimental.jax2tf import jax2tf
  from jax.interpreters import mlir
  import numpy
  import tensorflow
label_desc: Use image node_id 3 for calling a global function with example usage: _call_tf_impl() and returns: list, tf, tf, tf, jax, jax, jax