OperationState new_state(candidate_op->getLoc(),
                               candidate_op->getName().getStringRef(), inputs,
                               output_types, candidate_op->getAttrs());
      for (int i = 0; i < candidate_op->getNumRegions(); ++i) {
        new_state.addRegion();
      }
      Operation* quantized_op = rewriter.create(new_state);
      if (candidate_op->getNumRegions() != 0) {

        encoding.getBounds()[i] == ShapedType::kDynamic) {
      return false;
    }
  }
  return true;
}

bool HasSymbolRefAttr(Operation* op) {
  for (const auto& attr : op->getAttrs()) {
    Attribute attr_value = attr.getValue();
    if (mlir::isa<SymbolRefAttr>(attr_value)) {
      return true;
    } else if (auto array_attr = mlir::dyn_cast<ArrayAttr>(attr_value)) {

      new_result_types.push_back(while_op.getResultTypes()[i]);
    }
  }

  auto new_while_op = OpBuilder(while_op).create<WhileOp>(
      while_op.getLoc(), new_result_types, new_operands, while_op->getAttrs());
  new_while_op.getCond().takeBody(while_op.getCond());
  new_while_op.getBody().takeBody(while_op.getBody());

  for (auto i = 0; i < n; ++i) {
    if (!while_op.getResult(i).use_empty()) {

  for (auto extra_operand : extra_operands)
    new_types.push_back(extra_operand.getType());

  auto new_while_op = OpBuilder(while_op).create<WhileOp>(
      while_op.getLoc(), new_types, operands, while_op->getAttrs());
  new_while_op.getCond().takeBody(while_op.getCond());
  new_while_op.getBody().takeBody(while_op.getBody());
  while_op.replaceAllUsesWith(
      new_while_op.getResults().take_front(while_op.getNumResults()));

  // Skip derived `operandSegmentSizes` attribute as custom print format of
  // operands holds enough information to calculate these variadic operand list
  // lengths.
  p.printOptionalAttrDict(
      getOperation()->getAttrs(),
      /*elidedAttrs=*/ArrayRef<StringRef>{kOperandSegmentSizesAttr});
  p << ' ';
  p.printRegion(getBody(), /*printEntryBlockArgs=*/false);
}

namespace {

      ConversionPatternRewriter &rewriter) const override {
    Value input = adaptor.getOperands()[0];
    rewriter.replaceOpWithNewOp<TF::IdentityOp>(
        op, input.getType(), adaptor.getOperands(), op->getAttrs());
    return success();
  }
};

struct ConvertReturn : public OpConversionPattern<func::ReturnOp> {
  using OpConversionPattern::OpConversionPattern;

  LogicalResult matchAndRewrite(

		watchTests: []*testWatchStruct{{basePod, false, ""}, {basePodAssigned, true, watch.Added}},
		pred: storage.SelectionPredicate{
			Label: labels.Everything(),
			Field: fields.ParseSelectorOrDie("spec.nodeName=bar"),
			GetAttrs: func(obj runtime.Object) (labels.Set, fields.Set, error) {
				pod := obj.(*example.Pod)
				return nil, fields.Set{"spec.nodeName": pod.Spec.NodeName}, nil
			},
		},
	}, {
		name:       "update",

void SetFunctionPrivate(func::FuncOp func) {
  func.setVisibility(SymbolTable::Visibility::Private);

  // The `tf_saved_model` attributes can only be applied to public functions.
  for (auto& attr : func->getAttrs()) {
    StringRef attr_name = attr.getName().getValue();
    if (attr_name.starts_with("tf_saved_model.")) {
      func->removeAttr(attr_name);
    }
  }

    // contraction, with two additions: the list of ops which have been fused
    // together; epsilon (only with FusedBatchNorm).
    std::vector<NamedAttribute> attrs = contraction->getAttrs();
    ArrayAttr fused_ops_attr = ArrayAttr::get(context, fused_ops);
    attrs.push_back(
        NamedAttribute(StringAttr::get(context, "fused_ops"), fused_ops_attr));

      auto input_iter = input_attrs.begin() + outer * inner_size;
      for (int64_t inner = 0; inner < inner_size; ++inner)
        out_attrs[out++] = *input_iter++;
    }
  }

  return DenseElementsAttr::get(output_type, out_attrs);
}

}  // end anonymous namespace

LogicalResult ConcatenationOp::verify() {
  ConcatenationOp op = *this;