LogicalResult matchAndRewrite(BatchMatMulOpType op,
                                PatternRewriter& rewriter) const override {
    Value input_lhs = op.getX();
    Value input_rhs = op.getY();

    // LHS and RHS must be a ranked tensor type
    auto lhs_type = mlir::dyn_cast<RankedTensorType>(input_lhs.getType());
    auto rhs_type = mlir::dyn_cast<RankedTensorType>(input_rhs.getType());

    if (!lhs_type || !rhs_type) return failure();

template <typename BatchMatMulOpType>
LogicalResult ConvertTFBatchMatMulOp<BatchMatMulOpType>::matchAndRewrite(
    BatchMatMulOpType op, PatternRewriter& rewriter) const {
  Value input_lhs = op.getX();
  Value input_rhs = op.getY();

  if (!mlir::isa<RankedTensorType>(input_lhs.getType())) {
    // LHS must be a ranked tensor type
    return failure();
  }
  if (!mlir::isa<RankedTensorType>(input_rhs.getType())) {

          input, permutation_tensor_op.getResult());
    };

    Value input_lhs = bmm_op.getX();
    Value input_rhs = bmm_op.getY();

    Value output_lhs =
        bmm_op.getAdjX() ? create_z_x_transpose_op(input_lhs) : input_lhs;

    // The rhs need to be transposed if adj_y == false AND this matmul will be
    // legalized to tfl.fully_connected

//       total_cost = 0
//       for input_subgraph in current_subgraph.input_subgraphs:
//         input_cost = kInfinity
//         for input_target in input_subgraphs.upported_targets:
//           # cost = aggregated cost for input_subgraph with transfer cost.
//           input_cost = min(input_cost, cost)
//         total_cost += input_cost
//       total_cost += current_subgraph.get_computation_cost(target)
//

  if (auto input_result = mlir::dyn_cast<mlir::OpResult>(src)) {
    auto* input_inst = GetIslandInnerOpOrSelf(input_result.getOwner());
    // Replaces the input node with NextIteration sink if it is a NextIteration
    // source.
    if (auto next_iter_source =
            llvm::dyn_cast<mlir::tf_executor::NextIterationSourceOp>(
                input_inst))
      input_inst = next_iter_source.GetSink();

  if (auto input_result = mlir::dyn_cast<mlir::OpResult>(src)) {
    auto* input_inst = GetIslandInnerOpOrSelf(input_result.getOwner());
    // Replaces the input node with NextIteration sink if it is a NextIteration
    // source.
    if (auto next_iter_source =
            llvm::dyn_cast<mlir::tf_executor::NextIterationSourceOp>(
                input_inst))
      input_inst = next_iter_source.GetSink();

                  "_1 = ", "tensorflow::ops::Placeholder(scope.WithOpName(\"",
                  arg.name(), "\"), ", type, ");\n");
              strings::StrAppend(
                  out, absl::Substitute("    InputList $0({$0_0, $0_1});\n",
                                        arg.name()));
            } else {
              strings::StrAppend(
                  out, "    auto ", arg.name(), " = ",

  };

  if (!can_convert_to_fully_connected(op)) {
    return false;
  }

  Value input_lhs = get_real_input_value(op.getX());
  Value input_rhs = get_real_input_value(op.getY());

  Value legalized_lhs =
      op.getAdjX() ? create_z_x_transpose_op(input_lhs) : input_lhs;

  // The rhs need to be transposed if adj_y == false AND this matmul will be
  // legalized to tfl.fully_connected

    const auto& api_def_arg = *FindInputArg(api_def.arg_order(i), api_def);
    arg_types.push_back(strings::StrCat(
        "::tensorflow::", ArgIsList(arg) ? "InputList" : "Input"));
    arg_names.push_back(AvoidCPPKeywords(api_def_arg.rename_to()));

    // TODO(keveman): Include input type information.
    StringPiece description = api_def_arg.description();

// it is not present.  The new list will be returned.
func setConditionInList(inputList []autoscalingv2.HorizontalPodAutoscalerCondition, conditionType autoscalingv2.HorizontalPodAutoscalerConditionType, status v1.ConditionStatus, reason, message string, args ...interface{}) []autoscalingv2.HorizontalPodAutoscalerCondition {
	resList := inputList
	var existingCond *autoscalingv2.HorizontalPodAutoscalerCondition