});
      return false;
    }
    // FusedMatMul kernel does not support grad_a/grad_b attrs
    if ((matmul->hasAttr("grad_a") &&
         mlir::cast<BoolAttr>(matmul->getAttr("grad_a")).getValue()) ||
        (matmul->hasAttr("grad_b") &&
         mlir::cast<BoolAttr>(matmul->getAttr("grad_b")).getValue())) {
      (void)rewriter.notifyMatchFailure(matmul, [&](Diagnostic &diag) {

    } else if (auto int_attr =
                   mlir::dyn_cast_or_null<IntegerAttr>(attr.second)) {
      fbb.Int(attr.first.data(), int_attr.getInt());
    } else if (auto bool_attr = mlir::dyn_cast_or_null<BoolAttr>(attr.second)) {
      fbb.Bool(attr.first.data(), bool_attr.getValue());
    } else if (auto string_attr =
                   mlir::dyn_cast_or_null<StringAttr>(attr.second)) {

// This pass extracts a CPU computation cluster with `_xla_outside_compilation`
// annotation from the head or tail of a TPU cluster.

namespace {

using mlir::Block;
using mlir::BlockArgument;
using mlir::BoolAttr;
using mlir::ModuleOp;
using mlir::OpBuilder;
using mlir::Operation;
using mlir::OperationPass;
using mlir::Region;
using mlir::StringAttr;
using mlir::Type;
using mlir::Value;
using mlir::WalkResult;

#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;

LogicalResult PartitionResourceReadsWrites(
    tf_device::ClusterFuncOp cluster_func) {
  bool use_spmd = false;
  if (auto use_spmd_attr = cluster_func->getAttrOfType<BoolAttr>(kUseSpmdAttr))
    use_spmd = use_spmd_attr.getValue();

  if (!use_spmd) return success();

  auto num_cores_per_replica_attr =
      cluster_func->getAttrOfType<IntegerAttr>(kNumCoresPerReplicaAttr);

// 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;

//===----------------------------------------------------------------------===//

def ReadVariableOfCast : Pat<
  (TF_ReadVariableOp:$src (TF_CastOp:$output $x, BoolAttr:$Truncate)),
  (TF_ReadVariableOp:$dest $x), [(HasOnlyReadVariableOpUsers $output)],
  [(CopyAttrs $src, $dest)]>;

//===----------------------------------------------------------------------===//

    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", []> {