// `ctx_0`, `ctx_1` contains `remote_device`.
  {
    const std::vector<std::string>& device_names = ListDeviceNames(ctx_0);
    ASSERT_TRUE(std::find(device_names.begin(), device_names.end(),
                          remote_device) != device_names.end());
  }

  {
    const std::vector<std::string>& device_names = ListDeviceNames(ctx_1);

	// difference that for some j, a[j]>b[j] and a[j+1]<b[j+1]. If the implementation
	// compares large chunks with wrong endianness, it gets wrong result.
	// no vector register is larger than 512 bytes for now
	const maxLength = 512
	a := make([]byte, maxLength)
	b := make([]byte, maxLength)
	// randomish but deterministic data. No 0 or 255.
	for i := 0; i < maxLength; i++ {

		return 0, errors.New("invalid register for shift operation")
	}
	return int64(reg&31)<<16 | int64(op)<<22 | int64(uint16(count)), nil
}

// ARM64RegisterArrangement constructs an ARM64 vector register arrangement.
func ARM64RegisterArrangement(reg int16, name, arng string) (int64, error) {
	var curQ, curSize uint16
	if name[0] != 'V' {
		return 0, errors.New("expect V0 through V31; found: " + name)
	}

limitations under the License.
==============================================================================*/

#include "tensorflow/c/eager/c_api_unified_experimental.h"

#include <vector>

#include "absl/container/flat_hash_map.h"
#include "absl/strings/str_cat.h"
#include "tensorflow/c/eager/c_api_unified_experimental_internal.h"
#include "tensorflow/c/tf_datatype.h"
#include "tensorflow/c/tf_status.h"

  // REMOTE, REMOTE, LOCAL on task0. When remote is true, function device is
  // placed on task1, Handle types are LOCAL, REMOTE, LOCAL on task1.
  int num_replicas = 3;
  std::vector<TFE_TensorHandle*> handles = {h0, h1, h2};
  TFE_TensorHandle* packed_handle =
      TFE_CreatePackedTensorHandle(ctx, handles.data(), &num_replicas, status);
  ASSERT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);

  // `ctx_0`, `ctx_1`, `ctx_2` contains `remote_device`.
  {
    const std::vector<std::string>& device_names = ListDeviceNames(ctx_0);
    ASSERT_TRUE(std::find(device_names.begin(), device_names.end(),
                          remote_device) != device_names.end());
  }

  {
    const std::vector<std::string>& device_names = ListDeviceNames(ctx_1);

</p>

<p>
If a vector instruction takes a length or an index as an argument then it will be the
first argument.
For example, <code>VLEIF</code> <code>$1,</code> <code>$16,</code> <code>V2</code> will load
the value sixteen into index one of <code>V2</code>.
Care should be taken when using vector instructions to ensure that they are available at
runtime.

  // We're testing our asEnumeration method against a known-good implementation.
  @SuppressWarnings("JdkObsolete")
  private static Enumeration<Integer> enumerate(int... ints) {
    Vector<Integer> vector = new Vector<>(Ints.asList(ints));
    return vector.elements();
  }

  public void testToString() {
    Iterator<String> iterator = Lists.newArrayList("yam", "bam", "jam", "ham").iterator();

  // We're testing our asEnumeration method against a known-good implementation.
  @SuppressWarnings("JdkObsolete")
  private static Enumeration<Integer> enumerate(int... ints) {
    Vector<Integer> vector = new Vector<>(Ints.asList(ints));
    return vector.elements();
  }

  public void testToString() {
    Iterator<String> iterator = Lists.newArrayList("yam", "bam", "jam", "ham").iterator();

  TF_AbstractOpSetOpType(fn_op, fn_name.c_str(), s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  // Build two abstract input tensors as function arguments.
  std::vector<TF_AbstractTensor*> func_args;
  {
    TFE_Context* eager_ctx =
        TF_ExecutionContextGetTFEContext(eager_execution_ctx, status.get());
    ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());