TF_Buffer* value = TF_NewBuffer();
  TF_OperationGetAttrTensorShapeProto(oper, "v", value, s_);
  EXPECT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
  EXPECT_EQ(bytes.length(), value->length);
  EXPECT_EQ(0, memcmp(bytes.data(), value->data, value->length));
  TF_DeleteBuffer(value);
}

TEST_F(CApiAttributesTest, TensorShapeProtoList) {
  std::string bytes1, bytes2;
  tensorflow::TensorShapeProto proto;

    CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
    const float* resolved_data =
        static_cast<const float*>(TF_TensorData(resolved));
    EXPECT_EQ(0, memcmp(m_float, resolved_data, 4 * sizeof(float)));
    TF_DeleteTensor(resolved);
    TFE_DeleteTensorHandle(copy_aliased);  // Note that this will delete copy.
    TFE_DeleteTensorHandle(on_host);
  }

    if (TF_GetCode(status.get()) != TF_OK) {
      ADD_FAILURE() << tag;
      continue;
    }
    EXPECT_EQ(TF_TensorByteSize(t), TF_TensorByteSize(tcopy)) << tag;
    EXPECT_EQ(
        0, memcmp(TF_TensorData(t), TF_TensorData(tcopy), TF_TensorByteSize(t)))
        << tag;
    TF_DeleteTensor(tcopy);
  }

  TF_DeleteDeviceList(devices);
  TF_DeleteTensor(t);
  TFE_DeleteTensorHandle(hcpu);

  TF_RETURN_IF_ERROR(GetValue(theta, &theta_tensor));

  // Get number of elements and fill data.
  int num_elems = TF_TensorElementCount(theta_tensor);
  vector<float> theta_data(num_elems);
  memcpy(theta_data.data(), TF_TensorData(theta_tensor),
         TF_TensorByteSize(theta_tensor));

  // Initialize space for the numerical gradient.
  vector<float> dtheta_approx(num_elems);

TFE_TensorHandle* TestScalarTensorHandle(TFE_Context* ctx, float value) {
  float data[] = {value};
  TF_Status* status = TF_NewStatus();
  TF_Tensor* t = TFE_AllocateHostTensor(ctx, TF_FLOAT, nullptr, 0, status);
  memcpy(TF_TensorData(t), &data[0], TF_TensorByteSize(t));
  TFE_TensorHandle* th = TFE_NewTensorHandleFromTensor(ctx, t, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TF_DeleteTensor(t);

  *len = debug_str.size();
  char* ret = static_cast<char*>(malloc(*len + 1));
  memcpy(ret, debug_str.c_str(), *len + 1);
  return ret;
}

char* TF_FunctionDebugString(TF_Function* func, size_t* len) {
  const auto& debug_str = DebugString(func->record->fdef());
  *len = debug_str.size();
  char* ret = static_cast<char*>(malloc(*len + 1));
  memcpy(ret, debug_str.c_str(), *len + 1);
  return ret;
}

  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());
  TF_Tensor* result_tensor = TFE_TensorHandleResolve(result_t, status.get());

  // Copy Tensor data into an array.
  float result_data[4] = {0};
  memcpy(&result_data[0], TF_TensorData(result_tensor),
         TF_TensorByteSize(result_tensor));

  int data_len = 4;  // length of result_data
  for (int i = 0; i < data_len; i++) {
    EXPECT_EQ(result_data[i], 0);
  }

  ASSERT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);
  TFE_DeleteTensorHandle(retvals[0]);
  float sum = 0;
  EXPECT_EQ(sizeof(sum), TF_TensorByteSize(t));
  memcpy(&sum, TF_TensorData(t), TF_TensorByteSize(t));
  TF_DeleteTensor(t);
  EXPECT_EQ(sum, 6.0);

  TFE_DeleteTensorHandle(h0);
  TFE_DeleteTensorHandle(h1);
  TFE_DeleteTensorHandle(h2);

  ASSERT_EQ(errors::OK, s.code()) << s.message();
  auto num_elem_numerical = TF_TensorElementCount(numerical_tensor);
  ASSERT_EQ(num_elem_numerical, num_grad);

  float* dnumerical = new float[num_elem_numerical]{0};
  memcpy(&dnumerical[0], TF_TensorData(numerical_tensor),
         TF_TensorByteSize(numerical_tensor));

  for (int j = 0; j < num_grad; j++) {
    ASSERT_NEAR(dnumerical[j], expected_grad[j], abs_error);
  }

        final boolean unicode = getFlag(NTLMSSP_NEGOTIATE_UNICODE);
        final String oemCp = unicode ? null : getOEMEncoding();

        final String domainName = getDomain();
        byte[] domainBytes = null;
        if (domainName != null && domainName.length() != 0) {
            domainBytes = unicode ? domainName.getBytes(UNI_ENCODING) : domainName.getBytes(oemCp);
            size += domainBytes.length;
        }