TensorShape expected_shape, error::Code expected_code) {
  Status status;
  NodeDef def;
  def.set_op("Bitcast");
  def.set_device(DEVICE_CPU);

  AttrValue typeAttr;
  SetAttrValue(input_tensor->dtype(), &typeAttr);

  AttrValue outTypeAttr;
  SetAttrValue(out_type, &outTypeAttr);

  (*def.mutable_attr())["T"] = typeAttr;
  (*def.mutable_attr())["type"] = outTypeAttr;

  def.add_input(

  }
  return ret;
}

string PrintTensor(const TensorProto& tensor_proto);

string PrintTensorProto(const TensorProto& proto);

string PrintAttrValue(StringPiece, const AttrValue& attr_value);

bool IsEmptyList(const AttrValue::ListValue& list);

string ToCamelCase(StringPiece str);

string SeparateNamespaces(StringPiece str);

// Returns a <string, bool> pair. The string is the C++ type name to be used for

  std::unique_ptr<DeviceMgr> device_mgr_;
  std::unique_ptr<FunctionLibraryDefinition> lib_def_;
  std::unique_ptr<ProcessFunctionLibraryRuntime> pflr_;

  std::unique_ptr<OpKernel> kernel_;
};

AttrValue BoolAttr(bool b) {
  AttrValue v;
  v.set_b(b);
  return v;
}

TEST_F(XlaKernelCreatorTest, OneFloatOneResourceArgument) {
  FunctionDef fdef = XTimesY();
  (*fdef.mutable_attr())["_XlaMustCompile"] = BoolAttr(true);

// Tests #tf_type.func attributes are exported as AttrValue.NameAttrList attributes
// with its attr field populated with nested attributes.

module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 458 : i32}} {
  func.func @main() {

#include "tensorflow/core/platform/status.h"

namespace tensorflow {

// Extracts the attributes of a MLIR operation and populates the converted
// attributes in a proto map<string, AttrValue>.
Status GetAttrValuesFromOperation(
    mlir::Operation* inst, llvm::StringRef name,
    const tensorflow::OpRegistrationData* op_reg_data,
    bool ignore_unregistered_attrs, AttrValueMap* attributes);

                                      {"T", DT_FLOAT},
                                      {"Tperm", DT_INT32},
                                  }}},
                                {{"c", "t0:y:0"}});

  AttrValue true_attribute;
  true_attribute.set_b(true);
  (*called_func.mutable_attr())[kXlaMustCompileAttr] = true_attribute;
  *library.add_function() = called_func;
  TF_ASSERT_OK(root.graph()->AddFunctionLibrary(library));

    .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
      c->set_output(0, c->input(0));
      c->set_output(1, c->Scalar());
      c->set_output(2, c->Scalar());

      const tensorflow::AttrValue* num_bins_attr;
      TF_RETURN_IF_ERROR(c->GetAttr("num_bins", &num_bins_attr));
      c->set_output(3, c->MakeShape({num_bins_attr->i()}));

      return absl::OkStatus();
    });

bool GetFunctionDef(TF_Function* func, tensorflow::FunctionDef* func_def);

bool GetAttrValue(TF_Operation* oper, const char* attr_name,
                  tensorflow::AttrValue* attr_value, TF_Status* s);

// Returns a sorted vector of std::pair<function_name, gradient_func> from
// graph_def.library().gradient()
std::vector<std::pair<string, string>> GetGradDefs(

                         error::Code expected_code) {
  // Initialize node used to fetch OpKernel
  Status status;
  NodeDef def;
  def.set_op("ScalarSummary");

  def.set_device(DEVICE_CPU);

  AttrValue valuesTypeAttr;
  SetAttrValue(values->dtype(), &valuesTypeAttr);
  (*def.mutable_attr())["T"] = valuesTypeAttr;

  def.add_input(strings::StrCat("input1: ", DataTypeString(tags->dtype())));

  }

  NodeDef node;
  auto builder = NodeDefBuilder("some_node", "Const")
                     .Attr("dtype", data_type)
                     .Attr("value", tensor);
  // Create a bad output shape attr.
  AttrValue shape_attr;
  TensorShapeProto* shape_proto = shape_attr.mutable_list()->add_shape();
  shape_proto->add_dim()->set_size(1);
  builder.Attr("_output_shapes", shape_attr);

  TF_RETURN_IF_ERROR(builder.Finalize(&node));