bool preserve_assert_op) {
    this->run_tfl_runtime_verification_ = run_tfl_runtime_verification;
    this->preserve_assert_op_ = preserve_assert_op;
  }

  /// Performs the lowering to TFLite dialect.
  void runOnOperation() override;
};

// Util that casts 'val' to Int32 by adding a cast Op.
Value CreateCastToInt32(Value val, Location loc, PatternRewriter& rewriter) {

	constValue.reset(OpInvalid)
	return lattice{bottom, nil}
}

func (t *worklist) visitValue(val *Value) {
	if !possibleConst(val) {
		// fast fail for always worst Values, i.e. there is no lowering happen
		// on them, their lattices must be initially worse Bottom.
		return
	}

	oldLt := t.getLatticeCell(val)
	defer func() {
		// re-visit all uses of value if its lattice is changed

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

      // TF2XLA fallback pattern doesn't support these op as MLIR hlo builder
      // doesn't override the necessary builder methods. These ops have simple
      // lowering pattern so this should be safe.
      TypeID::get<TF::CrossReplicaSumOp>(),
      TypeID::get<TF::InfeedDequeueTupleOp>(),
      TypeID::get<TF::OutfeedEnqueueTupleOp>(),
      TypeID::get<TF::XlaShardingOp>(),

				// In the top-level case (ii=0) this can happen
				// because a composite variable was split into pieces,
				// and we're looking at a piece. We can also see
				// return temps (~r%d) that were created during
				// lowering, or unnamed params ("_").
				v.ChildIndex = int32(synthCount)
				synthCount++
			}
		}
	}

	// Make a second pass through the progs to compute PC ranges for
	// the various inlined calls.

  remat.RunGreedyAlgorithm(/*max_cost=*/1, /*max_block_length=*/1,
                           /*min_savings=*/1);
  // Only as single remat is done -- this will be the best one possible,
  // lowering the profile by 8 instead of the maximum 1 + 2 + 4 + 8.
  EXPECT_THAT(remat.GetMemProfile(),
              ElementsAreArray({1, 3, 7, 15, 23, 23, 15, 15, 7, 3, 1}));
}

TEST_F(GreedyRematTest, SimpleForbiddenOps) {

                // generated, which requires a container source to be provided. Without a facade class, function IR symbols will have
                // an `IrExternalPackageFragment` parent, which trips up code generation during IR lowering.
                val psiSourceFile =
                    descriptor.toSourceElement.containingFile as? PsiSourceFile ?: return super.getContainerSource(descriptor)

        "When specified to true, if the tensorlist ops has unspecified batch "
        "size, this pass will assume that the batch size is one to proceed "
        "tensorlist op lowering (default true)">,
      Option<"enable_dynamic_update_slice_", "enable-dynamic-update-slice",
             "bool", "false", "When specified to true, lower TensorListSetItem with "

	// the rules in late lower run after the general rules.
	{"lower", "late lower"},
	// late lower may generate some values that need to be CSEed.
	{"late lower", "lowered cse"},
	// checkLower must run after lowering & subsequent dead code elim
	{"lower", "checkLower"},
	{"lowered deadcode", "checkLower"},
	{"late lower", "checkLower"},
	// late nilcheck needs instructions to be scheduled.
	{"schedule", "late nilcheck"},

  // TODO(b/157054714): When a better abstraction instead of _TPUCompileMlirOp
  // and _XlaRecvAtHostOp and _XlaSendFromHostOp are used, update to a more
  // structured lowering.
  old_parallel_execute.walk(
      [&](TF::_XlaCompileMlirPlaceholderProgramKeyOp key_op) {
        key_op.replaceAllUsesWith(compile_op->getResult(1));
        key_op.erase();
      });

template <typename UniformQuantizedConvolutionOp>
FailureOr<SmallVector<NamedAttribute>> ConvertToMhloConvolutionOpAttrs(
    UniformQuantizedConvolutionOp op, PatternRewriter &rewriter) {
  // TODO(b/261005147): Update the lowering logic after migration to mhlo
  // ConvolutionDimensionNumbers.
  ::tensorflow::UniformQuantizedConvolutionDimensionNumbersAttr dnums_input;
  if (!dnums_input.ParseFromString(std::string(op.getDimensionNumbers()))) {