"//tensorflow/compiler/tf2xla/kernels:xla_ops",
        "//tensorflow/core:framework",
        "//tensorflow/core:test_main",
        "//tensorflow/core/protobuf/tpu:compile_metadata_proto_cc",
        "//tensorflow/core/tpu/kernels/xla:host_compute_ops",
        "@com_google_absl//absl/status",
        "@com_google_absl//absl/strings:string_view",
        "@llvm-project//mlir:FuncDialect",

        "//tensorflow/core:protos_all_cc",
        "//tensorflow/core/ir/types:Dialect",
        "//tensorflow/core/platform:error_payloads",
        "//tensorflow/core/platform:logging",
        "//tensorflow/core/platform:random",
        "//tensorflow/core/protobuf/tpu:compile_metadata_proto_cc",
        "//tensorflow/core/protobuf/tpu:dynamic_padding_proto_cc",
        "//tensorflow/core/tpu:tpu_embedding_optimization_parameters_utils",

                     const std::vector<Output>& grad_inputs,
                     std::vector<Output>* grad_outputs) {
  string mode;
  TF_RETURN_IF_ERROR(GetNodeAttr(op.node()->attrs(), "mode", &mode));
  grad_outputs->push_back(tensorflow::ops::internal::MirrorPadGrad(
      scope, grad_inputs[0], op.input(1), mode));
  grad_outputs->push_back(NoGradient());
  return scope.status();
}
REGISTER_GRADIENT_OP("MirrorPad", MirrorPadGrad);

#include "xla/shape_util.h"
#include "tensorflow/core/framework/tensor_shape.pb.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/lib/core/status.h"
#include "tensorflow/core/lib/core/status_test_util.h"
#include "tensorflow/core/lib/io/path.h"
#include "tensorflow/core/platform/env.h"
#include "tensorflow/core/platform/resource_loader.h"
#include "tensorflow/core/platform/test.h"

namespace tensorflow {

#include "tensorflow/compiler/jit/shape_inference_helpers.h"
#include "tensorflow/core/common_runtime/shape_refiner.h"
#include "tensorflow/core/framework/function.h"
#include "tensorflow/core/framework/node_def_util.h"
#include "tensorflow/core/framework/shape_inference.h"
#include "tensorflow/core/framework/tensor.pb.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/framework/tensor_shape.pb.h"
#include "tensorflow/core/framework/types.h"

#include "tensorflow/c/experimental/pluggable_profiler/pluggable_profiler.h"
#include "tensorflow/c/tf_status_helper.h"
#include "tensorflow/core/platform/status.h"
#include "tensorflow/core/profiler/lib/profiler_interface.h"
#include "tensorflow/core/profiler/protobuf/xplane.pb.h"
#include "tsl/profiler/protobuf/profiler_options.pb.h"

namespace tensorflow {
namespace profiler {

#include "tensorflow/cc/experimental/libtf/impl/tensor_spec.h"

#include "absl/hash/hash_testing.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/platform/test.h"

namespace tf {
namespace libtf {
namespace impl {

TEST(TensorSpecTest, TestSupportsAbslHash) {
  tensorflow::PartialTensorShape unknown_shape;

        "//tensorflow/core:lib_proto_parsing",
        "//tensorflow/core:protos_all_cc",
        "//tensorflow/core:tflite_portable_logging",
        "//tensorflow/core/common_runtime:core_cpu_base_no_ops",
        "//tensorflow/core/common_runtime/eager:context",
        # (yongtang) The graph_optimization_pass_registration needs to be part
        # of a shared object that will be loaded whenever `import tensorflow`

#include "mlir/IR/Builders.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_attributes.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/tensor.pb.h"
#include "tensorflow/core/framework/tensor_shape.pb.h"
#include "tensorflow/core/protobuf/struct.pb.h"

namespace tensorflow {

using tsl::StatusOr;

// Converts an TensorFlow tensor proto into an MLIR elements attribute.

==============================================================================*/

#include "tensorflow/c/ops.h"
#include "tensorflow/c/tf_status.h"
#include "tensorflow/core/framework/registration/registration.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/macros.h"

static void scalar_summary_shape_inference_fn(TF_ShapeInferenceContext* ctx,
                                              TF_Status* status) {