*   Transpose now supports 6D tensors.
    *   Float LSTM now supports diagonal recurrent tensors: https://arxiv.org/abs/1903.08023

*   `tf.experimental.dtensor`:

    *   Coordination service now works with `dtensor.initialize_accelerator_system`, and enabled by default.
    *   Add `tf.experimental.dtensor.is_dtensor` to check if a tensor is a DTensor instance.

*   `tf.data`:

  TFE_DeleteOp(matmul_op);
  // TODO(bgogul): Add some death tests where status is not OK.
}

TEST_F(ShapeInferenceTest, InfersShapesFromInputTensors) {
  // Prepare some tensors for shape.
  TF_Tensor* tensor_1X6 = Int32Tensor({1, 6});
  CHECK_EQ(TF_OK, TF_GetCode(status_)) << TF_Message(status_);
  TF_Tensor* tensor_1X1X6 = Int32Tensor({1, 1, 6});

    // Input tensors
    const std::vector<std::pair<string, Tensor>>& input_pairs,
    // Output tensors
    const std::vector<string>& output_tensor_names, TF_Tensor** c_outputs,
    // Target nodes
    const std::vector<string>& target_oper_names, TF_Buffer* run_metadata,
    TF_Status* status) {
  const int noutputs = output_tensor_names.size();
  std::vector<Tensor> outputs(noutputs);
  Status result;

    Run(inputs, {{output, 0}}, {expected_result});
  }

  // Run the host graph, which now contains a function and check that
  // outputs are as expected.
  // 'T' stands for 'tensor' since the outputs are tensors, not scalars.
  void RunT(const std::vector<std::pair<TF_Operation*, TF_Tensor*>>& inputs,
            std::initializer_list<TF_Output> outputs,
            const std::vector<std::vector<int32_t>>& expected_results) {

                                s_);
  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);

  const char* tensor_data[] = {&tensor1[0], &tensor2[0]};
  const size_t tensor_size[] = {TF_ARRAYSIZE(tensor1), TF_ARRAYSIZE(tensor2)};
  const int64_t* tensor_dims[] = {&dims1[0], &dims2[0]};
  const size_t tensor_ndims[] = {ndims1, ndims2};
  for (int i = 0; i < 2; ++i) {
    TF_Tensor* v = values[i];

  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);
  {

};

struct LoggedTensor {
  TFE_TensorHandle* tensor;
  LoggedTensor() = delete;
  explicit LoggedTensor(TFE_TensorHandle* tensor) : tensor(tensor) {}
  ~LoggedTensor() { TFE_DeleteTensorHandle(tensor); }
};

int64_t LoggedTensorDim(void* data, int dim_index, TF_Status* status) {
  return TFE_TensorHandleDim(reinterpret_cast<LoggedTensor*>(data)->tensor,
                             dim_index, status);
}

  // tensor are combined into a single entry.
  const TensorSliceReader::VarToShapeMap& GetVariableToShapeMap() const;

  // Returns a map from variable names to their data types.  Slices of a
  // partitioned tensor are combined into a single entry.
  const TensorSliceReader::VarToDataTypeMap& GetVariableToDataTypeMap() const;

  // Attempts to look up the tensor named "name" and stores the found result in

                          const char* name) {
  unique_tensor_ptr tensor(BoolTensor(v), TF_DeleteTensor);
  return Const(tensor.get(), graph, s, name);
}

TF_Operation* ScalarConst(int32_t v, TF_Graph* graph, TF_Status* s,
                          const char* name) {
  unique_tensor_ptr tensor(Int32Tensor(v), TF_DeleteTensor);
  return Const(tensor.get(), graph, s, name);
}

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

  TF_Tensor* tensor =
      TF_AllocateTensor(TF_FLOAT, dims.data(), dims.size(), sizeof(float));
  float tensor_data[] = {1};
  memcpy(TF_TensorData(tensor), tensor_data, TF_TensorByteSize(tensor));
  TFE_TensorHandle* tensor_handle = TFE_NewTensorHandle(tensor, status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);