unsigned char* is_list,
                                                    TF_Status* status);
// Get an attribute type given an op name; a fusion of TFE_NewOp and
// TFE_OpGetAttrType for use from Python without the overhead of the individual
// calls and memory management of TFE_Op.
TF_CAPI_EXPORT extern TF_AttrType TFE_OpNameGetAttrType(

        available automatically, without requiring an explicit `with
        tf.device(“/gpu:0”)`) (Fixes #14133)
    *   `tf.GradientTape` has moved out of contrib.
*   `tf.keras`:
    *   Added the fashion mnist dataset.
    *   New data preprocessing functions: `image/random_brightness`,
        `sequence/TimeseriesGenerator`, and `text/hashing_trick`.
*   Accelerated Linear Algebra (XLA):

- e2e tests that require SSH may now be used against clusters that have nodes without external IP addresses by setting the environment variable `KUBE_SSH_BASTION` to the `host:port` of a machine that is allowed to SSH to those nodes.  The same private key that the test would use is used for the bastion host.  The test connects to the bastion and then tunnels another SSH connection to the node. ([#72286](https://github.com/kubernetes/kubernetes/pull/72286), [@smarterclayton](https://github.com/smarterclayton))...

from the value obtained by executing and rounding the instructions individually.
An explicit floating-point type <a href="#Conversions">conversion</a> rounds to
the precision of the target type, preventing fusion that would discard that rounding.
</p>

<p>
For instance, some architectures provide a "fused multiply and add" (FMA) instruction

from the value obtained by executing and rounding the instructions individually.
An explicit <a href="#Numeric_types">floating-point type</a> <a href="#Conversions">conversion</a> rounds to
the precision of the target type, preventing fusion that would discard that rounding.
</p>

<p>
For instance, some architectures provide a "fused multiply and add" (FMA) instruction

A survey conducted in early 2019 by the working group (WG) Long Term Support (LTS) showed that a significant subset of Kubernetes end-users fail to upgrade within the previous 9-month support period. 
A yearly support period provides the cushion end-users appear to desire, and is more in harmony with familiar annual planning cycles.

## Known Issues