code: def get_session_handle(data, name=None):
      """Return the handle of `data`.
  
      This is EXPERIMENTAL and subject to change.
  
      Keep `data` "in-place" in the runtime and create a handle that can be
      used to retrieve `data` in a subsequent run().
  
      Combined with `get_session_tensor`, we can keep a tensor produced in
      one run call in place, and use it as the input in a future run call.
  
      Args:
        data: A tensor to be stored in the session.
        name: Optional name prefix for the return tensor.
  
      Returns:
        A scalar string tensor representing a unique handle for `data`.
  
      Raises:
        TypeError: if `data` is not a Tensor.
  
      Example:
  
      ```python
      c = tf.multiply(a, b)
      h = tf.compat.v1.get_session_handle(c)
      h = sess.run(h)
  
      p, a = tf.compat.v1.get_session_tensor(h.handle, tf.float32)
      b = tf.multiply(a, 10)
      c = sess.run(b, feed_dict={p: h.handle})
      ```
  
      """
      if not isinstance(data, tensor_lib.Tensor):
          raise TypeError("`data` must be of type Tensor.")
  
      # Colocate this operation with data.
      with ops.colocate_with(data):
          return gen_data_flow_ops.get_session_handle(data, name=name)
  
imports: import numpy
  from tensorflow.core.framework import resource_handle_pb2
  from tensorflow.python.client import pywrap_tf_session
  from tensorflow.python.framework import device
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gen_data_flow_ops
  from tensorflow.python.util import compat
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 2 for calling a global function with example usage: get_session_handle(data, name) and returns: gen_data_flow_ops