ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
  auto* arg1 = TF_AddFunctionParameter(graph_ctx, TF_FLOAT, {-1, nullptr}, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  // Create a first "Add" computing `arg0 + arg1`.
  TF_AbstractTensor* add_output1;
  {
    // Build an abstract operation, inputs and output.
    auto* add_op = TF_NewAbstractOp(graph_ctx);
    TF_AbstractOpSetOpType(add_op, "Add", s);

    TFE_ContextSetExecutorForThread(ctx, executor);

    TFE_DeleteExecutor(executor);
    TFE_DeleteContext(ctx);
  }
  TF_DeleteStatus(status);
}

TEST(CAPI, Function_ident_CPU) {
  // First create a simple identity function.
  TF_Graph* function_graph = TF_NewGraph();
  TF_OperationDescription* arg_descr =
      TF_NewOperation(function_graph, "Placeholder", "arg");
  TF_SetAttrType(arg_descr, "dtype", TF_INT32);

    if (device_index == 0) {
      first_op_output_count = per_device_output_tensors.rbegin()->size();
    } else {
      if (first_bad_status == nullptr &&
          per_device_output_tensors.rbegin()->size() != first_op_output_count) {
        first_bad_status.reset(TF_NewStatus());
        TF_SetStatus(first_bad_status.get(), TF_INTERNAL,
                     "Parallel ops produced different numbers of tensors.");
      }
    }
  }

looking through interesting issues. If you are not sure of where to start, then
start by trying one of the smaller/easier issues here i.e.
[issues with the "good first issue" label](https://github.com/tensorflow/tensorflow/labels/good%20first%20issue)
and then take a look at the

void TestConnectToCluster(bool keep_localhost_for_first_connect) {
  // Fail fast on GetStatus requests so we can get errors instead of timeout
  // when updating cluster with non-exsitent worker
  tensorflow::setenv("GRPC_FAIL_FAST", "TRUE", /*overwrite=*/1);

  const string first_name =
      keep_localhost_for_first_connect ? "localhost" : "abc";
  tensorflow::ServerDef server_def = GetServerDef(first_name, 1);

    const std::unordered_map<int64_t, int64_t>& op_missing_tensor) {
  std::vector<int64_t> result;
  for (auto& op_entry : op_tape) {
    if (op_missing_tensor.find(op_entry.first) == op_missing_tensor.end()) {
      result.push_back(op_entry.first);
    }
  }
  return result;
}

template <typename Gradient, typename BackwardFunction, typename TapeTensor>
Status InitialGradients(

  auto iter = builder->attr_names.insert(attr_name).first;
  builder->Set(*iter, static_cast<tensorflow::DataType>(value));
}

void TF_AttrBuilderSetTypeList(TF_AttrBuilder* builder, const char* attr_name,
                               const TF_DataType* values, int num_values) {
  auto iter = builder->attr_names.insert(attr_name).first;
  builder->Set(*iter, tensorflow::gtl::ArraySlice<const tensorflow::DataType>(

// can correspond to the data contained in another dimension in on-host
// representation. The dimensions are indexed using the standard TensorFlow
// major-to-minor order (slowest varying dimension first),
// not the XLA's minor-to-major order.
// On-device dimensions can be padded. TFE_TensorDebugInfoOnDeviceDim returns
// the number of elements in a dimension after padding.

    if (!enabled_) {
      return absl::OkStatus();
    }
    if (first_call_) {
      first_call_ = false;
      return absl::OkStatus();
    }
    return tensorflow::errors::Internal("Graph pass runs for more than once!");
  }

 private:
  bool first_call_ = true;
};

// After the graph pass is registered, it takes effect globally and can affect

        tensorflow::unwrap(handles[i]);
    if (tensorflow::CustomDeviceTensorHandle::classof(unwrapped_handle)) {
      // One of the inputs we're trying to pack is on a custom device. We'll let
      // the first custom device we see handle all of the packing.
      auto* custom_device_handle =
          tensorflow::down_cast<tensorflow::CustomDeviceTensorHandle*>(
              unwrapped_handle);