# Tensor Handle Operations.

TensorFlow provides several operators that allows the user to keep tensors "in-place" across run calls.

### tf.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:

c = tf.mul(a, b)
h = tf.get_session_handle(c)
h = sess.run(h)

p, a = tf.get_session_tensor(h.handle, tf.float32)
b = tf.mul(a, 10)
c = sess.run(b, feed_dict={p: h.handle})


### tf.get_session_tensor(handle, dtype, name=None)

Get the tensor of type dtype by feeding a tensor handle.

This is EXPERIMENTAL and subject to change.

Get the value of the tensor from a tensor handle. The tensor is produced in a previous run() and stored in the state of the session.

##### Args:
• handle: The string representation of a persistent tensor handle.
• dtype: The type of the output tensor.
• name: Optional name prefix for the return tensor.
##### Returns:

A pair of tensors. The first is a placeholder for feeding a tensor handle and the second is the tensor in the session state keyed by the tensor handle.

• Example:
c = tf.mul(a, b)
h = tf.get_session_handle(c)
h = sess.run(h)

p, a = tf.get_session_tensor(h.handle, tf.float32)
b = tf.mul(a, 10)
c = sess.run(b, feed_dict={p: h.handle})


### tf.delete_session_tensor(handle, name=None)

Delete the tensor for the given tensor handle.

This is EXPERIMENTAL and subject to change.

Delete the tensor of a given tensor handle. The tensor is produced in a previous run() and stored in the state of the session.

##### Args:
• handle: The string representation of a persistent tensor handle.
• name: Optional name prefix for the return tensor.
##### Returns:

A pair of graph elements. The first is a placeholder for feeding a tensor handle and the second is a deletion operation.