try {
            // Check if DiSNI is available on the classpath
            Class.forName("com.ibm.disni.RdmaActiveEndpointGroup");

            // Additional checks could include:
            // - Verifying RDMA devices are available
            // - Testing basic RDMA operations
            // - Checking for required native libraries

            return true;
        } catch (ClassNotFoundException e) {

// Controls how to act when we try to run an operation on a given device but
// some input tensors are not on that device.
// LINT.IfChange
// Note: Keep in sync with internal copy of enum in eager/context.h.
typedef enum TFE_ContextDevicePlacementPolicy {
  // Running operations with input tensors on the wrong device will fail.
  TFE_DEVICE_PLACEMENT_EXPLICIT = 0,
  // Copy the tensor to the right device but log a warning.
  TFE_DEVICE_PLACEMENT_WARN = 1,

    TF_Status status;
    // Let this custom device choose the device to pin this op on if it
    // implements the pinning function.
    if (device_.shall_pin_to_this_device != nullptr) {
      return device_.shall_pin_to_this_device(tensorflow::wrap(op), &status);
    }
    return errors::Unimplemented("No custom device pinning implementation.");
  }

 private:
  TFE_Context* context_;

environments. TensorFlow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer, whereas in previous “parameter server” designs the management of...

				info.Name = devName
				qst, err := bfs.SysBlockDeviceQueueStats(devName)
				if err != nil { // Mostly not found error
					// Check if there is a parent device:
					//   e.g. if the mount is based on /dev/nvme0n1p1, let's calculate the
					//        real device name (nvme0n1) to get its sysfs information
					parentDevPath, e := os.Readlink("/sys/class/block/" + devName)
					if e == nil {

  LoggingDevice* device = new LoggingDevice;
  device->arrived_flag = arrived_flag;
  device->executed_flag = executed_flag;
  device->device_name = name;
  device->underlying_device = "/job:localhost/replica:0/task:0/device:CPU:0";
  device->strict_scope_placement = strict_scope_placement;
  TFE_RegisterCustomDevice(context, custom_device, name, device, status);
}

=====

JCIFS is an Open Source client library that implements the CIFS/SMB networking protocol in 100% Java.
This fork from [jcifs-ng](https://github.com/AgNO3/jcifs-ng) merges backward compatibility with legacy SMB devices while supporting modern SMB2/SMB3 protocols. Key features include SMB2 support, per-context configuration, SLF4J logging, NTLMSSP/Kerberos authentication, and streaming operations.

## Version

    Throwable thrownReflectionFailure = null;
    try {
      helper = new SafeAtomicHelper();
    } catch (Throwable reflectionFailure) { // sneaky checked exception
      // Some Android 5.0.x Samsung devices have bugs in JDK reflection APIs that cause
      // getDeclaredField to throw a NoSuchFieldException when the field is definitely there.
      // For these users fallback to a suboptimal implementation, based on synchronized. This will

 * @author Martin Buchholz
 * @since 11.0
 */
public class AtomicDouble extends Number {
  private static final long serialVersionUID = 0L;

  // We would use AtomicLongFieldUpdater, but it has issues on some Android devices.
  private transient AtomicLong value;

  /**
   * Creates a new {@code AtomicDouble} with the given initial value.
   *
   * @param initialValue the initial value
   */

[CUDA-enabled GPU cards](https://www.tensorflow.org/install/gpu) *(Ubuntu and
Windows)*:

```
 pip install tensorflow
```

Other devices (DirectX and MacOS-metal) are supported using
[Device Plugins](https://www.tensorflow.org/install/gpu_plugins#available_devices).

A smaller CPU-only TensorFlow package is also available:

```
 pip install tensorflow-cpu
```