float* m_float = static_cast<float*>(TF_TensorData(m_data));
  TF_DeviceList* devices = TFE_ContextListDevices(ctx, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  int num_devices = TF_DeviceListCount(devices);
  for (int d = 0; d < num_devices; ++d) {
    const char* name = TF_DeviceListName(devices, d, status);
    CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

 * Prefer recent JDK builds, and results are temperamental to slight environment changes.
 * Different instructions and configuration may be required for other hardware devices.
 *
 * Using a yubikey device as a SSL key store.
 * https://lauri.võsandi.com/2017/03/yubikey-for-ssh-auth.html
 *
 * Using PKCS11 support in the JDK.
 * https://tersesystems.com/blog/2018/09/08/keymanagers-and-keystores/
 *

  // devices.
  virtual absl::Status Dim(int dim_index, int64_t* dim) const = 0;

  // Returns the device which created the handle.
  virtual const char* DeviceName(absl::Status* status) const = 0;
  // Returns the device where the tensor was placed.
  virtual const char* BackingDeviceName(absl::Status* status) const = 0;
  // Returns the device type which created the handle.

	export GOIOS_APP_ID=YYYYYYYY.some.bundle.id
	export GOIOS_TEAM_ID=ZZZZZZZZ

If you have multiple devices connected, specify the device UDID with the GOIOS_DEVICE_ID
variable. Use `idevice_id -l` to list all available UDIDs. Then, setting GOARCH to arm64
will select the device:

	GOOS=ios GOARCH=arm64 CGO_ENABLED=1 CC_FOR_TARGET=$(pwd)/../misc/ios/clangwrap.sh ./all.bash

// so there is no risk of polluting the entire cache with data accessed once.
// Another goal of DirectIO is to minimize the mutation of data by the kernel
// before issuing IO to underlying devices. ZFS users often enable features like
// compression and checksumming which currently necessitates mutating data in
// the kernel.
//
// DirectIO semantics for a filesystem like ZFS would be quite different than

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...

  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);
}

    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_;

  ASSERT_TRUE(executed);
  TFE_DeleteTensorHandle(retval);

  // Custom device: inputs in different custom devices fails.
  matmul.reset(MatMulOp(context.get(), hcustom0.get(), hcustom1.get()));
  num_retvals = 1;
  TFE_Execute(matmul.get(), &retval, &num_retvals, status.get());
  ASSERT_NE(TF_OK, TF_GetCode(status.get()));
  ASSERT_TRUE(absl::StrContains(TF_Message(status.get()), custom0));

				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 {