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();
}

    (TFL_BatchMatMulOp:$bmm_tmp_output
      (TFL_ReshapeOp:$initial_shape_change $input, (Arith_ConstantOp $s0)),
      $rhs, $adj_x, $adj_y, $bool_attr),
    (Arith_ConstantOp $s1)),
  (TFL_BatchMatMulOp $input, $rhs, $adj_x, $adj_y, $bool_attr),
  [(HasRank<2> $rhs),
   (HasRank<2> $initial_shape_change),
   (BroadcastDimsProductEqual<0> $input, $initial_shape_change),

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;

	Name    [1024]int8
	_       [3]byte
}

type Attrlist struct {
	Bitmapcount uint16
	Reserved    uint16
	Commonattr  uint32
	Volattr     uint32
	Dirattr     uint32
	Fileattr    uint32
	Forkattr    uint32
}

const (
	PathMax = 0x400
)

type RawSockaddrInet4 struct {
	Len    uint8
	Family uint8
	Port   uint16
	Addr   [4]byte /* in_addr */
	Zero   [8]int8
}

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

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

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

	Name    [1024]int8
	_       [3]byte
}

type Attrlist struct {
	Bitmapcount uint16
	Reserved    uint16
	Commonattr  uint32
	Volattr     uint32
	Dirattr     uint32
	Fileattr    uint32
	Forkattr    uint32
}

const (
	PathMax = 0x400
)

type RawSockaddrInet4 struct {
	Len    uint8
	Family uint8
	Port   uint16
	Addr   [4]byte /* in_addr */
	Zero   [8]int8
}

    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()),