Roland Shoemaker <******@****.***> 1715710616 -0700

    return std::vector<double>(vec.begin(), vec.end());
  }

  return std::vector<double>();
}

// Handles the case when the DenseElementsAttr doesn't exist, and when it
// doesn't returns a vector of length `default_size` all with the same value
// `default_value`.
template <typename T>
static inline std::vector<T> GetOptionalVector(

class CalibrationStatisticsSaverTest : public OpsTestBase {};

TEST_F(CalibrationStatisticsSaverTest, MissingOutputPath) {
  std::vector<std::string> ids{"1"};
  std::vector<int32_t> calibration_methods{
      CalibrationOptions::CALIBRATION_METHOD_AVERAGE_MIN_MAX};

  std::vector<NodeDefBuilder::NodeOut> inputs;
  inputs.emplace_back("min", 0, DT_FLOAT);
  inputs.emplace_back("max", 0, DT_FLOAT);

}

static mlir::Attribute BuildRankedTensorAttr(std::vector<int64_t> shape,
                                             std::vector<bool> value,
                                             mlir::Builder builder) {
  // The implementation of getBoolVectorAttr is flawed, so we bypass it here
  std::vector<llvm::APInt> extendVec;
  extendVec.resize(value.size());

Status PreparePjRtExecutableArguments(
    int num_missing_prefix_ctx_inputs, const std::vector<int>& input_mapping,
    const std::vector<const Tensor*>& inputs,
    const absl::flat_hash_map<int, const Tensor*>& variable_snapshots,
    xla::PjRtClient* pjrt_client, xla::PjRtDevice* pjrt_device,
    bool use_pjrt_tensor_buffer, std::vector<xla::PjRtBuffer*>* args,
    std::vector<std::unique_ptr<xla::PjRtBuffer>>* owned_args,

std::vector<int> ConstantsVector(OpKernelConstruction* ctx) {
  DataTypeVector constant_types;
  OP_REQUIRES_OK_RETURN(ctx, std::vector<int>(),
                        ctx->GetAttr("Tconstants", &constant_types));
  std::vector<int> constants(constant_types.size());
  std::iota(constants.begin(), constants.end(), 0);
  return constants;
}

std::vector<int> ResourcesVector(OpKernelConstruction* ctx) {

    const XlaShapeLayoutHelpers::ShapeDeterminationFns
        shape_determination_funcs,
    const std::vector<tensorflow::TensorShape>& arg_shapes,
    std::vector<tpu::ShardingAndIndex>* arg_core_mapping,
    std::vector<std::vector<xla::Shape>>* per_core_arg_shapes,
    xla::CompileOnlyClient* client,
    XlaCompiler::CompilationResult* compilation_result) {

  auto client =
      xla::ClientLibrary::GetOrCreateCompileOnlyClient(cpu_platform).value();

  std::vector<TensorShape> arg_shapes;
  TPUCompileMetadataProto metadata_proto;
  bool use_tuple_args = true;
  std::vector<ShardingAndIndex> arg_core_mapping;
  std::vector<std::vector<xla::Shape>> per_core_arg_shapes;
  std::vector<std::unique_ptr<mlir::Pass>> custom_legalization_passes;

  // This doesn't actually compile correctly.

  mlir::MLIRContext context;
  mlir::quant::QuantizationSpecs quant_specs;

  // Parse input arrays.
  std::vector<string> node_names;
  std::vector<string> node_dtypes;
  std::vector<std::optional<std::vector<int>>> node_shapes;
  std::vector<std::optional<double>> node_mins;
  std::vector<std::optional<double>> node_maxs;

  // Populate quantization specs.
  TF_RETURN_IF_ERROR(internal::PopulateQuantizationSpecs(

    const toco::ModelFlags& model_flags, toco::TocoFlags& toco_flags,
    mlir::quant::QuantizationSpecs* quant_specs,
    std::vector<std::string>* node_names, std::vector<std::string>* node_dtypes,
    std::vector<std::optional<std::vector<int>>>* node_shapes,
    std::vector<std::optional<double>>* node_mins,
    std::vector<std::optional<double>>* node_maxs) {
  quant_specs->inference_input_type =