operand.getType(), operand);
    yield.setOperand(0, identity.getOutput());
  } else {
    auto identity_n = builder.create<TF::IdentityNOp>(
        island.getLoc(), yield.getOperandTypes(), yield.getOperands());
    for (auto it : llvm::enumerate(identity_n.getResults()))
      yield.setOperand(it.index(), it.value());
  }
}

    // Walk all operations and nested regions to find accessed gradient sources
    // for function arguments.
    func.walk([&](Operation* op) {
      if (llvm::isa<TF::IdentityOp, TF::IdentityNOp>(op)) {
        op->replaceAllUsesWith(op->getOperands());
        return;
      }
      if (auto grad = llvm::dyn_cast<TF::TensorArrayGradV3Op>(op)) {

// such nodes to carry information.
void RemovePassthroughOp(Block &block) {
  for (auto &op : llvm::make_early_inc_range(block)) {
    if (isa<TF::IdentityOp, TF::IdentityNOp>(op) || IsCastOfResource(op)) {
      op.replaceAllUsesWith(op.getOperands());
      op.erase();
    }
  }
}

using LocalVarOp = std::variant<TF::VarHandleOp, TF::MlirLocalVarOp>;

  while (auto result = mlir::dyn_cast<OpResult>(v)) {
    if (!(allow_other_use || v.hasOneUse())) break;
    auto op = result.getDefiningOp();
    if (!llvm::isa<TF::IdentityOp, TF::IdentityNOp>(op)) {
      break;
    }
    v = op->getOperand(result.getResultNumber());
    if (skipped) skipped->insert(op);
  }
  return v;
}

    TypeID::get<TF::_FusedConv2DOp>(),
    TypeID::get<TF::GatherNdOp>(),
    TypeID::get<TF::GatherV2Op>(),
    TypeID::get<TF::GreaterEqualOp>(),
    TypeID::get<TF::IdentityOp>(),
    TypeID::get<TF::IdentityNOp>(),
    TypeID::get<TF::InplaceUpdateOp>(),
    TypeID::get<TF::InvertPermutationOp>(),
    TypeID::get<TF::IRFFTOp>(),
    TypeID::get<TF::L2LossOp>(),
    TypeID::get<TF::LegacyCallOp>(),

    llvm::StringMap<PartitionedCallStackOpsInfo>*
        decomposed_partitioned_call_callees) {
  for (auto& op : llvm::make_early_inc_range(block->getOperations())) {
    if (llvm::isa<TF::IdentityOp, TF::IdentityNOp>(&op)) {
      // Removes identity nodes in the block. The device computation does not
      // need such nodes to carry information.
      op.replaceAllUsesWith(op.getOperands());
      op.erase();

  for (auto& op : llvm::make_early_inc_range(block->getOperations())) {
    // TODO(yuanzx): Add a pass to remove identities in device computation.
    if (llvm::isa<TF::IdentityOp, TF::IdentityNOp, TF::StopGradientOp>(&op)) {
      op.replaceAllUsesWith(op.getOperands());
      op.erase();
    } else if (auto list = llvm::dyn_cast<TF::EmptyTensorListOp>(&op)) {

  // TODO(haoliang): add an interface for DatasetOp to avoid the following
  // enumeration.
  // Iteratively tracing upwards if parent op is `IdentityOp` or `IdentityNOp`.
  while (
      llvm::isa_and_nonnull<IdentityOp, IdentityNOp>(value.getDefiningOp())) {
    value = value.getDefiningOp()->getOperand(
        mlir::cast<OpResult>(value).getResultNumber());
  }

  Operation* op = value.getDefiningOp();

    }
    return success();
  }
};

// Bypasses IdentityN op.
class ConvertIdentityNOp : public OpRewritePattern<TF::IdentityNOp> {
 public:
  using OpRewritePattern<TF::IdentityNOp>::OpRewritePattern;
  LogicalResult matchAndRewrite(TF::IdentityNOp op,
                                PatternRewriter &rewriter) const override {
    rewriter.replaceOp(op, op.getOperands());
    return success();

namespace mlir {
namespace TF {

namespace {
// Returns the equivalent Value skipping through identity nodes.
Value LookThroughIdentity(Value result) {
  while (isa_and_nonnull<IdentityOp, IdentityNOp>(result.getDefiningOp())) {
    auto op_result = result.cast<OpResult>();
    result = op_result.getOwner()->getOperand(op_result.getResultNumber());
  }
  return result;
}