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);

	if err != nil {
		return 0, fmt.Errorf("query() failure for '%s': %v", query, err)
	}

	log.Debugf("executing query: %s  result:%s", query, val)

	switch v := val.(type) {
	case model.Vector:
		if v.Len() < 1 {
			log.Debugf("no values for query: %s", query)
			return 0, nil
		}

		return float64(v[0].Value), nil
	default:
		return 0, errors.New("bad metric value type returned for query")

                         status.get());
  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());

  // Create two vectors with different lengths
  std::vector<float> size_two_value{1., 2.};
  std::vector<float> size_three_value{1., 2., 3.};
  TensorHandlePtr size_two(
      VectorFloatTensorHandle(size_two_value, status.get()));

</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.

		}
	} else {
		return errors.New("invalid register and extension combination")
	}
	return 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)
	}

---

## **Typer**, FastAPI CLI

<a href="https://typer.tiangolo.com" target="_blank"><img src="https://typer.tiangolo.com/img/logo-margin/logo-margin-vector.svg" style="width: 20%;"></a>

  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());

  job_def->mutable_tasks()->at(task_index) =
      tensorflow::strings::StrCat("localhost:", port);
}

void CheckTFE_TensorHandleHasFloats(TFE_TensorHandle* handle,
                                    const std::vector<float>& expected_values) {
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
      TF_NewStatus(), TF_DeleteStatus);
  TF_Tensor* t = TFE_TensorHandleResolve(handle, status.get());