// ShapedType for the leading values with ShapedType in the results of the
// nodes. Set it to true if the returned NodeDef will be executed by the linked
// TF Eager runtime.
absl::StatusOr<std::unique_ptr<NodeDef>> ConvertTFDialectOpToNodeDef(
    mlir::Operation* inst, llvm::StringRef name,
    bool ignore_unregistered_attrs);

}  // namespace tensorflow

      StringRef custom_options, Location loc, std::string& op_name,
      SmallVectorImpl<NamedAttribute>& attributes,
      tensorflow::NodeDef& node_def) {
    // The flexbuffer contains a vector where the first elements is the
    // op name and the second is a serialized NodeDef.
    const uint8_t* const opt_data =
        reinterpret_cast<const uint8_t*>(custom_options.data());
    const size_t opt_size = custom_options.size();

// 'input_permutation' and 'output_permutation' are initialized to the identity
// permutation. 'nodedef' is the NodeDef for the call to the function under
// construction, provided to allow additional attributes to be set.
// The rewrite may also change the NodeDef's operator name, and that
// name will be used as the name of the generated function.
typedef std::function<Status(

      }
    }
  }
  return found_dtype && found_value;
}

bool IsAddN(const tensorflow::NodeDef& node_def, int n) {
  if (node_def.op() != "AddN" || node_def.name() != "add" ||
      node_def.input_size() != n) {
    return false;
  }
  bool found_t = false;
  bool found_n = false;
  for (const auto& attr : node_def.attr()) {
    if (attr.first == "T") {
      if (attr.second.type() == tensorflow::DT_INT32) {

bool HasLiftedArgs(const FunctionDef& function_def) {
  return absl::c_any_of(function_def.node_def(), [](const NodeDef& node_def) {
    return (node_def.op() == "Placeholder" &&
            node_def.attr().find(kXlaLiftedArgOutsideCompilationAttrName) !=
                node_def.attr().end());
  });
}

// Find lifted arguments in a function body and their corresponding outside
// compilation nodes.

func.func @main() {
  // The operation does not have any attributes, but TensorFlow OpDef expects
  // a `dtype` to be added on the NodeDef. We verify that we correctly use the
  // DerivedAttr to populate the NodeDef.
  // CHECK:      key: "dtype"
  // CHECK-NEXT: value {
  // CHECK-NEXT:   type: DT_FLOAT
  // CHECK:   float_val: 2
  // CHECK:      key: "dtype"
  // CHECK-NEXT: value {

PartialTensorShape S(std::initializer_list<int64_t> dims) {
  return PartialTensorShape(dims);
}

PartialTensorShape Unknown() { return PartialTensorShape(); }

TEST(OpsTest, ShapeInferenceWithRank) {
  NodeDef def;
  shape_inference::InferenceContext c(0, def, MakeOpDef(1, 0),
                                      {S({10, 20, 30})}, {}, {}, {});

  shape_inference::ShapeHandle in0 = c.input(0);

                                       node_def->mutable_attr()));

  return node_def;
}

absl::StatusOr<std::unique_ptr<NodeDef>> Exporter::GetReturnNode(
    FuncOp function, Value operand, unsigned index, llvm::StringRef name) {
  auto node_def = std::make_unique<NodeDef>();
  if (!name.empty())
    node_def->set_name(std::string(ParseTensorName(name.str()).node()));
  else

                                       node_def->mutable_attr()));

  return node_def;
}

absl::StatusOr<std::unique_ptr<NodeDef>> Exporter::GetReturnNode(
    FuncOp function, Value operand, unsigned index, llvm::StringRef name) {
  auto node_def = std::make_unique<NodeDef>();
  if (!name.empty())
    node_def->set_name(std::string(ParseTensorName(name.str()).node()));
  else

// dialect back into a TensorFlow valid op name.
absl::StatusOr<llvm::StringRef> GetTensorFlowOpName(llvm::StringRef);

// Converts an MLIR operation to TensorFlow NodeDef with given node name. This
// name should be unique to the graph it is being inserted into.
absl::StatusOr<std::unique_ptr<NodeDef>> GetOperationNodeDef(
    mlir::Operation* inst, llvm::StringRef name);

// Converts MLIR attributes with values to their tensorflow equivalent.