# Tensor Handle Operations
Note: Functions taking `Tensor` arguments can also take anything accepted by
[`tf.convert_to_tensor`](framework.md#convert_to_tensor).
[TOC]
## Tensor Handle Operations.
TensorFlow provides several operators that allows the user to keep tensors
"in-place" across run calls.
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### `tf.get_session_handle(data, name=None)` {#get_session_handle}
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.
Below is a simple example:
```python
c = tf.mul(a, b)
h = tf.get_session_handle(c)
h = sess.run(h)
p, a = tf.get_session_tensor(tf.float32)
b = tf.mul(a, 10)
c = sess.run(b, feed_dict={p: h.handle})
```
##### 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.
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### `tf.get_session_tensor(dtype, name=None)` {#get_session_tensor}
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:
* `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.
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### `tf.delete_session_tensor(name=None)` {#delete_session_tensor}
Delete the tensor by feeding a 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:
* `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.