void setupResponse ( Response resp );


    /**
     * @return whether signing has been negotiated
     */
    boolean isSigningNegotiated ();


    /**
     * @param cap
     * @return whether capability is negotiated
     */
    boolean haveCapabilitiy ( int cap );


    /**
     * @return the send buffer size
     */
    int getSendBufferSize ();


    /**

import jcifs.SmbTransport;


/**
 * @author mbechler
 *
 */
public interface SmbTransportInternal extends SmbTransport {

    /**
     * @param cap
     * @return whether the transport has the given capability
     * @throws SmbException
     */
    boolean hasCapability ( int cap ) throws SmbException;


    /**
     * @return whether the transport has been disconnected
     */
    boolean isDisconnected ();

        'you want to build with.\nYou can find the compute capability of your '
        'device at: https://developer.nvidia.com/cuda-gpus. Each capability '
        'can be specified as "x.y" or "compute_xy" to include both virtual and'
        ' binary GPU code, or as "sm_xy" to only include the binary '
        'code.\nPlease note that each additional compute capability '

        }
        return null;
    }


    /**
     * 
     * Only call this method while holding a tree handle
     * 
     * @param cap
     * @return whether the capability is available
     * @throws SmbException
     */
    public boolean hasCapability ( int cap ) throws SmbException {
        try ( SmbSessionImpl s = getSession() ) {
            if ( s != null ) {

 * We need to look at Maven, and Nexus as use cases and figure out what each of them needs to be
 * able to do
 *
 * - now what is really the difference between this and loading a component in plexus - custom
 * classloading capability - remote resolution of dependencies - do we want a model for sharing
 * information among plugins, is this more like an extension point - do we need a sort of bus for

# This is the same as the official TensorFlow builds.
# See https://developer.nvidia.com/cuda-gpus#compute
# `compute_XY` enables PTX embedding in addition to SASS. PTX
# is forward compatible beyond the current compute capability major
# release while SASS is only forward compatible inside the current
# major release. Example: sm_80 kernels can run on sm_89 GPUs but
# not on sm_90 GPUs. compute_80 kernels though can also run on sm_90 GPUs.

  EXPECT_EQ(4, product[1]);
  EXPECT_EQ(6, product[2]);
  EXPECT_EQ(8, product[3]);

  // When we turn on grappler, confirm that the tf.Add has been rewritten into a
  // tf.Mul.
  // This capability of checking the executed op names is currently only enabled
  // for TFRT debug build, for performance and simplicity reasons.
  if (use_tfrt) {
    TF_Buffer* buf = TF_NewBuffer();

- GlusterFS PersistentVolumes sources can now reference endpoints in any namespace using the `spec.glusterfs.endpointsNamespace` field. Ensure all kubelets are upgraded to 1.13+ before using this capability. ([#60195](https://github.com/kubernetes/kubernetes/pull/60195), [@humblec](https://github.com/humblec))

- The certificate signer no longer accepts ca.key passwords via the `CFSSL_CA_PK_PASSWORD` environment variable. This capability was not prompted by user request, never advertised, and recommended against in the security audit. ([#84677](https://github.com/kubernetes/kubernetes/pull/84677), [@mikedanese](https://github.com/mikedanese))

* Fix `cstring.h` missing file issue with the Libtensorflow archive.

# Release 2.17.0

## TensorFlow

### Breaking Changes

* GPU
    * Support for NVIDIA GPUs with compute capability 5.x (Maxwell generation) has been removed from TF binary distributions (Python wheels).

### Major Features and Improvements