getOperation().walk([&](IslandOp island_op) {
    auto parent_func = island_op->getParentOfType<func::FuncOp>();
    auto skip_island_outlining =
        parent_func->getAttrOfType<BoolAttr>(mlir::TF::kSkipIslandOutlining);
    if (skip_island_outlining && skip_island_outlining.getValue()) {
      // Island was marked to be skipped.
      return WalkResult::advance();
    }

#include "tensorflow/core/lib/monitoring/gauge.h"

namespace tensorflow {
namespace tf2xla {
namespace internal {

namespace {

using mlir::Block;
using mlir::BoolAttr;
using mlir::Dialect;
using mlir::LogicalResult;
using mlir::MLIRContext;
using mlir::ModuleOp;
using mlir::Operation;
using mlir::OperationName;
using mlir::OperationPass;
using mlir::Pattern;

        /*fused_activation_function=*/rewriter.getStringAttr("NONE"),
        /*weights_format=*/rewriter.getStringAttr("DEFAULT"),
        /*keep_num_dims=*/rewriter.getBoolAttr(true),
        /*asymmetric_quantize_inputs=*/mlir::BoolAttr());
    rewriter.replaceOp(bmm_op, {fc_op.getResult(0)});

    return success();
  };
};

// Converts batch_matmul operation with a ones tensor to a reduce_sum.
struct ConvertBatchMatMulOpToReduceSum

// BatchNorm op patterns.
//===----------------------------------------------------------------------===//

def FalseBoolAttr : AttrConstraint<CPred<"!$_self.cast<BoolAttr>().getValue()">>;
def TrueBoolAttr : AttrConstraint<CPred<"$_self.cast<BoolAttr>().getValue()">>;

def CastValueToI64: NativeCodeCall<
  "CastValueToI64($0.getLoc(), $1, &$_builder)">;

def CastValueToElementType: NativeCodeCall<

        mlir::DenseIntElementsAttr::get(static_shape_type, static_shape);
    return rewriter.create<TF::ConstOp>(loc, static_shape_attr).getOutput();
  }

  // If the shape is not static, create a new ShapeOp.
  BoolAttr false_attr = rewriter.getBoolAttr(false);
  return rewriter
      .create<TF::ShapeOp>(loc, input,
                           /*use_32bit=*/false_attr)
      .getOutput();
}

using OptionalOpShardingVector =
    llvm::SmallVector<std::optional<OpShardingVariant>, 8>;
using llvm::StringRef;
using mlir::Block;
using mlir::BlockArgument;
using mlir::BoolAttr;
using mlir::Builder;
using mlir::IntegerAttr;
using mlir::LogicalResult;
using mlir::ModuleOp;
using mlir::Operation;
using mlir::OpOperand;
using mlir::OpResult;
using mlir::RankedTensorType;

    The `tf.IfrtCall` kernel uses the output $array_key.
    Other ops executed by TFRT may make use of $tensor_future.
  }];

  let arguments = (ins
    TFTensorType:$variable,
    DefaultValuedAttr<BoolAttr, "false">:$used_by_host
  );

  let results = (outs
    TFTensorType:$array_key,
    MlrtFutureType: $tensor_future
  );
}

def IfrtRestoreVariableOp: TensorflowMlrt_Op<"ifrt_restore_variable", []> {

                          lhs_dot_dimensions_info.FlattenedOutDimensionSize()},
                      llvm::ArrayRef<int64_t>{
                          rhs_dot_dimensions_info.FlattenedOutDimensionSize()});
  BoolAttr false_attr = rewriter.getBoolAttr(false);
  auto matmul = rewriter.create<TFL::BatchMatMulOp>(
      loc, RankedTensorType::get(matmul_shape, result_type.getElementType()),

                                      ImplicitLocOpBuilder& builder,
                                      bool is_lhs) {
  auto operand_type = mlir::cast<ShapedType>(operand.getType());
  BoolAttr true_attr = builder.getBoolAttr(true);
  auto operand_shape = builder.create<TF::ShapeOp>(operand, true_attr);
  const int64_t operand_rank = operand_type.getRank();

           << "]";

  return success();
}

bool VariableIsInitialized(TF::VarHandleOp var_handle_op) {
  auto is_variable_initialized =
      var_handle_op->getAttrOfType<BoolAttr>("_is_initialized");
  // Assume variable is initialized if attribute is not set.
  // There are paths that doesn't mark the variables. All variables
  // that doesn't have the attribute will be promoted.