auto ReluGradModel = BuildGradModel(ReluModel, registry_);

  float X_vals[] = {1.0f, 2.0f, 3.0f, -5.0f, -4.0f, -3.0f, 2.0f, 10.0f, -1.0f};
  int64_t X_dims[] = {3, 3};
  AbstractTensorHandlePtr X;
  {
    AbstractTensorHandle* X_raw;
    status_ = TestTensorHandleWithDims<float, TF_FLOAT>(
        immediate_execution_ctx_.get(), X_vals, X_dims, 2, &X_raw);

TEST(CAPI, TestFromProto) {
  Tensor t_cc(DT_FLOAT, TensorShape({2, 3}));
  t_cc.flat<float>().setConstant(1.0);
  tensorflow::TensorProto t_proto;
  t_cc.AsProtoField(&t_proto);

  TF_Buffer* t_buffer = TF_NewBuffer();
  TF_CHECK_OK(MessageToBuffer(t_proto, t_buffer));

  const int num_bytes = 6 * sizeof(float);
  float* values =
      reinterpret_cast<float*>(tensorflow::cpu_allocator()->AllocateRaw(

      TF_AbstractTensorGetEagerTensor(result, status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());
  TF_Tensor* result_tensor = TFE_TensorHandleResolve(result_t, status.get());
  float* result_value = static_cast<float*>(TF_TensorData(result_tensor));
  EXPECT_EQ(*result_value, 4.0);

  TF_DeleteTensor(result_tensor);
  TF_DeleteAbstractTensor(result);
  TF_DeleteOutputList(o);
  TF_DeleteExecutionContext(ctx);

  ASSERT_EQ(1, num_retvals);
  EXPECT_EQ(TF_FLOAT, TFE_TensorHandleDataType(value_handle));
  EXPECT_EQ(0, TFE_TensorHandleNumDims(value_handle, status));
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  float value = 0.0f;
  TF_Tensor* t = TFE_TensorHandleResolve(value_handle, status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  ASSERT_EQ(sizeof(float), TF_TensorByteSize(t));

    TF_Tensor* resolved = TFE_TensorHandleResolve(on_host, status);
    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);

    status_ = TestScalarTensorHandle<float, TF_FLOAT>(
        immediate_execution_ctx_.get(), 1.0f, &x1_raw);
    ASSERT_EQ(errors::OK, status_.code()) << status_.message();
    x1.reset(x1_raw);
  }

  AbstractTensorHandlePtr x2;
  {
    AbstractTensorHandle* x2_raw = nullptr;
    status_ = TestScalarTensorHandle<float, TF_FLOAT>(
        immediate_execution_ctx_.get(), 1.0f, &x2_raw);

      } else {
        EXPECT_EQ(dltensor_out->strides[i],
                  dltensor_out->shape[i + 1] * dltensor_out->strides[i + 1]);
      }
    }
  }
  const float* data_in = static_cast<const float*>(dltensor_in->data);
  const float* data_out = static_cast<const float*>(dltensor_out->data);
  for (size_t j = 0; j < num_elements; ++j) {
    EXPECT_EQ(data_out[j], data_in[j]);
  }

  TFE_CallDLManagedTensorDeleter(dlm_out);

    ctx.reset(ctx_raw);
  }

  AbstractTensorHandlePtr x;
  {
    AbstractTensorHandle* x_raw = nullptr;
    float data[] = {0., 0., 0., 0., 0., 0., 0., 0};
    int64_t dim_sizes[] = {2, 4};
    Status s = TestTensorHandleWithDims<float, TF_FLOAT>(ctx.get(), data,
                                                         dim_sizes, 2, &x_raw);
    ASSERT_EQ(errors::OK, s.code()) << s.message();

#include "tensorflow/core/util/port.h"

using tensorflow::string;
using tensorflow::tstring;

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);

    ASSERT_EQ(num_elem_numerical, num_elem_analytical);

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