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", []> {

  auto operands = adaptor.getOperands();
  assert(operands.size() == 2 && "equal op has two operands");
  auto ctx = getContext();
  if (operands[0] == operands[1]) return BoolAttr::get(ctx, true);
  return BoolAttr::get(ctx, false);
}

OpFoldResult ConstOp::fold(FoldAdaptor adaptor) {
  auto operands = adaptor.getOperands();
  (void)operands;
  assert(operands.empty() && "constant has no operands");

                          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.