See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

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

namespace tensorflow {

std::string AbstractTensorHandle::DebugString() const {
  PartialTensorShape shape;
  Status s = Shape(&shape);
  std::string shape_string;
  if (!s.ok()) {

See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

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

namespace tensorflow {

std::string ImmediateExecutionTensorHandle::DebugString() const {
  PartialTensorShape shape;
  std::string shape_string;
  if (Shape(&shape).ok()) {

  // Convert from AbstractTensor to TF_Tensor.
  TF_Tensor* theta_tensor;
  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.

  TFE_TensorHandle* input_eager =
      TestScalarTensorHandle<T, datatype>(eager_ctx, value);
  *tensor =
      unwrap(TF_CreateAbstractTensorFromEagerTensor(input_eager, status.get()));
  return absl::OkStatus();
}

// Places data from `t` into *result_tensor.
Status GetValue(AbstractTensorHandle* t, TF_Tensor** result_tensor);
}  // namespace tensorflow

void GradientTape<Gradient, BackwardFunction, TapeTensor>::DeleteTrace(
    int64_t tensor_id) {
  auto it = tensor_usage_.find(tensor_id);
  if (it == tensor_usage_.end()) {
    return;
  }
  it->second--;
  if (it->second != 0) {
    return;
  }
  tensor_usage_.erase(it);
  auto tensor_op_it = tensor_tape_.find(tensor_id);
  if (tensor_op_it == tensor_tape_.end()) {
    return;
  }

      numerical_grad.reset(numerical_grad_raw);
    }

    TF_Tensor* numerical_tensor;
    s = GetValue(numerical_grad.get(), &numerical_tensor);
    ASSERT_EQ(errors::OK, s.code()) << s.message();
    auto num_elem_numerical = TF_TensorElementCount(numerical_tensor);

    TF_Tensor* analytical_tensor;
    s = GetValue(outputs[i], &analytical_tensor);
    ASSERT_EQ(errors::OK, s.code()) << s.message();

          Request((mockServer.secureEndpoint + "/person?name=peter").toHttpUrl()),
        ).execute()

      assertThat(response.body.string()).contains("Peter the person")
      assertThat(response.protocol).isEqualTo(Protocol.HTTP_1_1)
    }
  }

  @Test
  fun testUrlConnectionDirect() {
    testRequest {
      val url = URI(mockServer.endpoint + "/person?name=peter").toURL()

        // ## Arrange ##
        Locale.setDefault(Locale.JAPANESE);
        final SQLException sqlException = new SQLException("some reason", "fooState", 7650);
        final SQLException sqlException2 = new SQLException("hoge reason", "barState", 7660);
        final SQLException sqlException3 = new SQLException("fuga reason", "bazState", 7670);
        sqlException.setNextException(sqlException2);
        sqlException2.setNextException(sqlException3);

  if (VLOG_IS_ON(1) && status->status.ok()) {
    tensorflow::Tensor tensor;
    if (tensorflow::TF_TensorToTensor(ret, &tensor).ok()) {
      VLOG(1) << "Dequeued tensor content: " << tensor.DebugString();
    }
  }
  return ret;
}

void TF_EnqueueNamedTensor(TF_Session* session, int tensor_id,
                           TF_Tensor* tensor, TF_Status* status) {
  assert(session);
  {

    ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
    TF_Tensor* expected_out0 = expected_csession->output_tensor(0);
    TF_Tensor* expected_out1 = expected_csession->output_tensor(1);

    CompareTensors(out0, expected_out0);
    CompareTensors(out1, expected_out1);
  }

  void CompareTensors(TF_Tensor* a, TF_Tensor* b) {
    float* a_data = static_cast<float*>(TF_TensorData(a));