const char* attr_name) {
  bool out = false;
  bool attr_value;
  if (TryGetNodeAttr(node->attrs(), attr_name, &attr_value)) {
    out |= attr_value;
  }

  if (flib_def->GetAttr(*node, attr_name, &attr_value).ok()) {
    out |= attr_value;
  }
  return out;
}

Status MarkForCompilationPassImpl::BuildInitialClusterSet() {

    }
    // Recreate the attributes dictionary to only contain "value".
    NamedAttrList attributes;
    attributes.append(NamedAttribute(value_str_attr, cst->getAttr("value")));
    cst->setAttrs(attributes.getDictionary(&getContext()));
  });

  auto& side_effect_analysis = getAnalysis<mlir::TF::SideEffectAnalysis>();
  for (auto func : getOperation().getOps<mlir::func::FuncOp>())

      for (const auto& attr_name : attr_names) {
        if (!global_iter_id_op->hasAttr(attr_name)) continue;
        offset->setAttr(attr_name, global_iter_id_op->getAttr(attr_name));
        new_global_iter_id->setAttr(attr_name,
                                    global_iter_id_op->getAttr(attr_name));
      }
      // Make the next function to get inlined use a different offset.
      ++offset_value;
    }

                "/"
            ), "A path prefix must not end with '/', as the routes will start with '/'"
        else:
            for r in router.routes:
                path = getattr(r, "path")  # noqa: B009
                name = getattr(r, "name", "unknown")
                if path is not None and not path:
                    raise FastAPIError(

  if (padding == tensorflow::Padding::EXPLICIT) {
    ArrayRef<Attribute> explicit_padding;
    ArrayAttr explicit_pad = mlir::dyn_cast_or_null<::mlir::ArrayAttr>(
        op->getAttr("explicit_paddings"));
    if (!explicit_pad) {
      explicit_pad = ::mlir::Builder(op->getContext()).getI64ArrayAttr({});
    }
    explicit_padding = explicit_pad.getValue();

           llvm::all_of(op->getResultTypes(), is_not_variant);
  };

  auto is_set_item_legal = [](Operation *op) {
    return op->hasAttr("resize_if_index_out_of_bounds") &&
           op->getAttr("resize_if_index_out_of_bounds")
               .cast<mlir::BoolAttr>()
               .getValue();
  };

  ConversionTarget target(*context);

    // folding), but we can still derive the shape of a constant tensor for
    // its attribute type.
    auto tensor_attr = mlir::cast<mlir::TypedAttr>(inst->getAttr("value"));
    llvm::ArrayRef<int64_t> shape_ref =
        mlir::cast<TensorType>(tensor_attr.getType()).getShape();
    if (mlir::failed(check_shape(shape_ref))) return std::nullopt;

template <typename CallOpClass>
static LogicalResult VerifyPartitionedCall(CallOpClass op,
                                           SymbolTableCollection &symbolTable) {
  SymbolRefAttr func = op->getAttr("f").template cast<SymbolRefAttr>();
  auto function = symbolTable.lookupNearestSymbolFrom<func::FuncOp>(op, func);
  if (!function) {
    return op.emitError("'f' attribute refers to an undefined function: ")

      // avoid compilation failure when exporting "layouts" attribute of the
      // corresponding InfeedDequeueTupleOp to a graph node.
      data_and_token->setAttr("layout", op->getAttr("layouts"));
    }
    llvm::SmallVector<Value> results;
    results.reserve(result_types.size());
    for (const auto &idx_and_type : llvm::enumerate(result_types)) {

        which would previously raise an error. This will correspond to an
        attribute name with an embedded '.' symbol (e.g. 'a.b'), which can only
        be accessed indirectly (e.g. through getattr and setattr). To set this
        up the user will first need to explicitly add the variable to the hparam
        object (e.g. "hparams.add_hparam(name='a.b', value=0.0)").