#include "tensorflow/core/framework/node_def.pb.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/platform/test.h"

using ::tensorflow::string;

typedef std::unique_ptr<TF_Tensor, decltype(&TF_DeleteTensor)>
    unique_tensor_ptr;

TF_Tensor* BoolTensor(int32_t v);

// Create a tensor with values of type TF_INT8 provided by `values`.
TF_Tensor* Int8Tensor(const int64_t* dims, int num_dims, const char* values);

// just nice to know that it compiles.
void compute(void* kernel, TF_OpKernelContext* ctx) {
  TF_Tensor* input;
  TF_Status* s = TF_NewStatus();
  TF_GetInput(ctx, 0, &input, s);
  TF_DeleteTensor(input);
  TF_DeleteStatus(s);
}

// Exercises tensorflow's C API.
int main(int argc, char** argv) {
  TF_InitMain(argv[0], &argc, &argv);

  struct TF_StringStream* s = TF_GetLocalTempDirectories();

  TFE_DeleteTensorHandle(retval_task0);
  float product[4] = {0};
  EXPECT_EQ(sizeof(product), TF_TensorByteSize(t));
  memcpy(&product[0], TF_TensorData(t), TF_TensorByteSize(t));
  TF_DeleteTensor(t);
  EXPECT_EQ(7, product[0]);
  EXPECT_EQ(10, product[1]);
  EXPECT_EQ(15, product[2]);
  EXPECT_EQ(22, product[3]);

  TFE_DeleteTensorHandle(h0_task0);
  TFE_DeleteTensorHandle(h1_task0);

  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);

  TFE_Executor* executor = TFE_ContextGetExecutorForThread(ctx);

  memcpy(TF_TensorData(t), data, TF_TensorByteSize(t));
  TFE_TensorHandle* th = TFE_NewTensorHandleFromTensor(ctx, t, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TF_DeleteTensor(t);
  TF_DeleteStatus(status);
  return th;
}

// Return a scalar tensor handle with given values.
template <class T, TF_DataType datatype>

         TF_TensorByteSize(numerical_tensor));

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

absl::Status MatMulModel(AbstractContext* ctx,
                         absl::Span<AbstractTensorHandle* const> inputs,
                         absl::Span<AbstractTensorHandle*> outputs) {

// `tensor_id`, associated with `session`. There must be a graph node named
// "fifo_queue_dequeue_<tensor_id>", to be executed by this API call.

// Caller must call TF_DeleteTensor() over the returned tensor. If the queue is
// empty, this call is blocked.
//
// Tensors are enqueued via the corresponding TF enqueue op.
// TODO(hongm): Add support for `timeout_ms`.

    int max_values, TF_Status* status);

// Gets the TF_Tensor valued attribute of `attr_name` of `oper`.
//
// Allocates a new TF_Tensor which the caller is expected to take
// ownership of (and can deallocate using TF_DeleteTensor).
TF_CAPI_EXPORT extern void TF_OperationGetAttrTensor(TF_Operation* oper,
                                                     const char* attr_name,
                                                     TF_Tensor** value,

  std::unique_ptr<float[]> actual_values(new float[expected_values.size()]);
  EXPECT_EQ(sizeof(float) * expected_values.size(), TF_TensorByteSize(t));
  memcpy(actual_values.get(), TF_TensorData(t), TF_TensorByteSize(t));
  TF_DeleteTensor(t);

  for (int i = 0; i < expected_values.size(); i++) {
    EXPECT_EQ(expected_values[i], actual_values[i])
        << "Mismatch in expected values at (zero-based) index " << i;
  }
}

                  "`inputs` must have a single output. Node split3 has "
                  "3 outputs. Encountered while creating function 'MyFunc'"),
      std::string(TF_Message(s_)));

  TF_DeleteTensor(tensor_123);
}

TEST_F(CApiFunctionTest, FunctionWithWhileLoop) {
  // Inputs to the while loop and the function as a whole
  TF_Operation* feed1 = Placeholder(func_graph_, s_, "feed1");