EXPECT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);

  TFE_TensorHandle* h0_task0 = TestMatrixTensorHandle(ctx);
  TFE_TensorHandle* h1_task0 = TestMatrixTensorHandle(ctx);
  std::vector<TFE_TensorHandle*> handles_task0;
  if (heavy_load_on_streaming_rpc) {
    // Send 50 tensor copy requests to simulate that there have been some RPC
    // requests been enqueued.
    for (int i = 0; i < 50; ++i) {

        .createTestSuite();
  }

  // We are testing Vector / testing our tests on Vector.
  @SuppressWarnings("JdkObsolete")
  private Test testsForVector() {
    return ListTestSuiteBuilder.using(
            new TestStringListGenerator() {
              @Override
              protected List<String> create(String[] elements) {
                return new Vector<>(MinimalCollection.of(elements));
              }
            })

        .createTestSuite();
  }

  // We are testing Vector / testing our tests on Vector.
  @SuppressWarnings("JdkObsolete")
  private Test testsForVector() {
    return ListTestSuiteBuilder.using(
            new TestStringListGenerator() {
              @Override
              protected List<String> create(String[] elements) {
                return new Vector<>(MinimalCollection.of(elements));
              }
            })

  bool ret = TF_GetCode(s) == TF_OK;
  if (ret) ret = attr_value->ParseFromArray(buffer->data, buffer->length);
  TF_DeleteBuffer(buffer);
  return ret;
}

std::vector<std::pair<string, string>> GetGradDefs(
    const tensorflow::GraphDef& graph_def) {
  std::vector<std::pair<string, string>> grads;
  for (const tensorflow::GradientDef& grad : graph_def.library().gradient()) {
    grads.emplace_back(grad.function_name(), grad.gradient_func());

            if (mechs != null) {
                final ASN1EncodableVector vector = new ASN1EncodableVector();
                for (final ASN1ObjectIdentifier mech : mechs) {
                    vector.add(mech);
                }
                fields.add(new DERTaggedObject(true, 0, new DERSequence(vector)));
            }
            final int ctxFlags = getContextFlags();
            if (ctxFlags != 0) {

        @CsvSource({ "1000, 500, 1, 512", // Minimal sectors per alloc
                "1000, 500, 8, 512", // Typical configuration
                "1000, 500, 64, 512", // Large allocation units
                "1000, 500, 8, 4096", // 4K sectors
                "1000, 500, 16, 4096", // Large sectors and allocation
                "1000, 0, 8, 512", // No free space

==============================================================================*/

#ifndef TENSORFLOW_C_EAGER_C_API_UNIFIED_EXPERIMENTAL_INTERNAL_H_
#define TENSORFLOW_C_EAGER_C_API_UNIFIED_EXPERIMENTAL_INTERNAL_H_

#include <vector>

#include "tensorflow/c/c_api.h"
#include "tensorflow/c/conversion_macros.h"
#include "tensorflow/c/eager/abstract_context.h"
#include "tensorflow/c/eager/abstract_operation.h"

	MOVL (AX)(X0*1), AX             // ERROR "invalid instruction"
	MOVL (AX)(Y0*1), AX             // ERROR "invalid instruction"
	// VSIB/VM is invalid without vector index.
	// TODO(quasilyte): improve error message (#21860).
	// "invalid VSIB address (missing vector index)"
	VPGATHERQQ Y2, (BP), Y1         // ERROR "invalid instruction"
	// AVX2GATHER mask/index/dest #UD cases.

     */
    @Override
    public Settings settings() {
        return client.settings();
    }

    /**
     * Gets term vectors for a document asynchronously.
     *
     * @param request the term vectors request
     * @return a future for the term vectors response
     */
    @Override
    public ActionFuture<TermVectorsResponse> termVectors(final TermVectorsRequest request) {

        @CsvSource({ "1000, 500, 600, 1, 512", // Minimal sectors per alloc
                "1000, 500, 600, 8, 512", // Typical configuration
                "1000, 500, 600, 64, 512", // Large allocation units
                "1000, 500, 600, 8, 4096", // 4K sectors
                "1000, 500, 600, 16, 4096", // Large sectors and allocation
                "1000, 0, 0, 8, 512", // No free space