//   . Legalizes control flow operations.
//   . Decomposes compound resource operations so that the only remaining
//     operations on resource variables are resource reads/writes..
//   . Replaces resource reads/writes with function inputs/outputs and
//     eliminates the use of resource variables.
//   . Legalizes the operations to XLA HLO operations.

namespace mlir {
namespace TFL {
namespace {
#define GEN_PASS_DEF_LEGALIZEVARIABLESPASS
#include "tensorflow/compiler/mlir/lite/transforms/passes.h.inc"

// Attribute name to identify whether variables should be legalized to TFLite or
// not.
const char kLegalizeTflVariables[] = "tfl._legalize_tfl_variables";

bool HasSupportedElementType(Operation* op) {
  return utils::IsSupportedVariableType(op);
}

  target.addLegalDialect<func::FuncDialect>();
  target.addLegalDialect<tensor::TensorDialect>();
  target.addLegalDialect<shape::ShapeDialect>();
  target.addLegalOp<func::CallOp>();

  // These ops are legalized in LegalizeTFCommunication after this and that pass
  // only operates on MHLO control flow ops.
  target.addLegalOp<TF::_XlaHostComputeMlirOp, TF::XlaSendToHostOp,
                    TF::XlaRecvFromHostOp>();

      index_lt.getLhs() != body.getArgument(1) ||
      index_lt.getRhs() != body.getArgument(3))
    return failure();

  return success();
}

// Returns true if the given reduce op can be legalized to ArgMax/ArgMin ops.
std::optional<bool> IsReduceOpLegal(mhlo::ReduceOp reduce_op) {
  if (succeeded(MatchReduceToArgMinMaxType1(reduce_op, true, true)) ||
      succeeded(MatchReduceToArgMinMaxType1(reduce_op, false, true)) ||

class OperationPass;
class Type;

namespace TFL {

// Creates an instance of the TensorFlow Lite dialect LegalizeTF pass.
// When the given run_tfl_runtime_verification value is true, it will check each
// TFL builtin op towards the TFL runtime capability and the incompatible TF ops
// will be left in the graph without getting legalized. If `preserve_assert_op`
// is true, the TF::AssertOp will not be removed.

  EXPECT_FALSE(IsDynamicPadderOp(TypeID::get<TF::ConstOp>()));
}

// This test is kind of odd. We go through all the Tensorflow types and check
// whether they are legalized with MLIR, TF2XLA, or both. Ideally the sets are
// disjoint, but until that happens, this tests ensures that the set doesn't
// grow.
TEST_F(LegalizationOpConfigTest, CountLoweringsSet) {
  int mlir_lowering_count = 0;

    }
    return success();
  }

  virtual bool IsValueInitValue(const DenseElementsAttr& attr) const = 0;
};

// Returns true if the given reduce op can be legalized to ArgMax/ArgMin ops.
std::optional<bool> IsReduceOpLegal(mhlo::ReduceOp reduce_op);

}  // namespace odml
}  // namespace mlir

    if (inserted_value) continue;
    // We can only clone the constant op or const->dequantize combo. The latter
    // case is useful for float16 quantization. Since all ops have been
    // legalized to tflite ops, so we only care about ConstOp or QConstOp or
    // mlir constant op.
    Operation* input_op = operand.getDefiningOp();
    if (input_op == nullptr) return failure();

    Attribute attr;

      TypeID::get<TF::ModOp>(),

      // MatrixDiagPartV3 should use the MLIR implementation due to performance.
      TypeID::get<TF::MatrixDiagPartV3Op>(),

      // Ops that are legalized in the old bridge using MlirXlaOpKernel
      TypeID::get<TF::AbsOp>(),
      TypeID::get<TF::AtanOp>(),
      TypeID::get<TF::AvgPool3DOp>(),
      TypeID::get<TF::BiasAddGradOp>(),
      TypeID::get<TF::CeilOp>(),

    Value output_lhs =
        bmm_op.getAdjX() ? create_z_x_transpose_op(input_lhs) : input_lhs;

    // The rhs need to be transposed if adj_y == false AND this matmul will be
    // legalized to tfl.fully_connected
    Value output_rhs =
        !bmm_op.getAdjY() ? create_z_x_transpose_op(input_rhs) : input_rhs;

    Type output_type = bmm_op.getResult().getType();