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);

    // Create C++ Tensor
    Tensor src(tensorflow::DT_STRING, TensorShape(dims));
    for (int64_t i = 0; i < src.NumElements(); ++i) {
      src.flat<tstring>()(i) = data[i];
    }
    TF_Tensor* dst = TF_TensorFromTensor(src, &status);
    ASSERT_TRUE(status.ok()) << status.message();

    // Convert back to a C++ Tensor and ensure we get expected output.
    Tensor output;

// that defines two inputs, "concat_dim" and "values" (in that order).
// You must use TF_AddInput() for the first input (since it takes a
// single tensor), and TF_AddInputList() for the second input (since
// it takes a list, even if you were to pass a list with a single
// tensor), as in:
//   TF_OperationDescription* desc = TF_NewOperation(graph, "Concat", "c");
//   TF_Output concat_dim_input = {...};

        "//tensorflow/core:reader_base",
        "//tensorflow/core:script_ops_op_lib",
        "//tensorflow/distribute/experimental/rpc/kernels:rpc_ops",
        "//tensorflow/dtensor/cc:dtensor_device_cc",
        "//tensorflow/dtensor/cc:tensor_layout",
        "//tensorflow/lite/c:common",
        "//tensorflow/lite/core/api",
        "//tensorflow/lite/delegates/flex:delegate",

// Specification for an expected edge.
// src is either:
// - input name (as it appears in FunctionDef)
// - name of output tensor (in nested "add:z:0" format)
// dst is either:
// - output name (as it appears in FunctionDef)
// - <name_of_node>:<index_of_this_input_into_node> (this looks the same as
//      output tensor naming, but it the index is actually an input index)
struct EdgeSpec : public std::pair<std::string, std::string> {