float max_percentile;
    OP_REQUIRES_OK(context, context->GetAttr("calibration_method",
                                             &calibration_method_value));
    OP_REQUIRES_OK(context, context->GetAttr("num_bins", &num_bins));
    OP_REQUIRES_OK(context,
                   context->GetAttr("min_percentile", &min_percentile));
    OP_REQUIRES_OK(context,
                   context->GetAttr("max_percentile", &max_percentile));

}

void CreateOp::print(OpAsmPrinter &p) {
  CreateOp op = *this;
  p << "(" << op.getInCh() << ") key("
    << op->getAttrOfType<mlir::IntegerAttr>("op_key").getInt() << ") device("
    << op->getAttr("device") << ") " << op->getAttr("op_name") << "()";

  fallback_common::PrintExecuteOpCommon(p, op);
  fallback_common::PrintExecuteOpFuncAttribute(p, op);

def test_signatures_consistency():
    base_sig = inspect.signature(APIRouter.get)
    for method_name in method_names:
        router_method = getattr(APIRouter, method_name)
        app_method = getattr(FastAPI, method_name)
        router_sig = inspect.signature(router_method)
        app_sig = inspect.signature(app_method)
        param: inspect.Parameter

    string file_name;
    string func_name;
    string node_name;
    OP_REQUIRES_OK(ctx, ctx->GetAttr("log_dir_path", &log_dir_path));
    OP_REQUIRES_OK(ctx, ctx->GetAttr("enabled", &enabled_));
    OP_REQUIRES_OK(ctx, ctx->GetAttr("file_name", &file_name));
    OP_REQUIRES_OK(ctx, ctx->GetAttr("func_name", &func_name));
    OP_REQUIRES_OK(ctx, ctx->GetAttr("node_name", &node_name));
    OP_REQUIRES_OK(ctx, ctx->env()->RecursivelyCreateDir(log_dir_path));

def test_route_classes():
    routes = {}
    for r in app.router.routes:
        assert isinstance(r, Route)
        routes[r.path] = r
    assert getattr(routes["/a/"], "x_type") == "A"  # noqa: B009
    assert getattr(routes["/a/b/"], "x_type") == "B"  # noqa: B009
    assert getattr(routes["/a/b/c/"], "x_type") == "C"  # noqa: B009


def test_openapi_schema():
    response = client.get("/openapi.json")

    .SetIsStateful()
    .SetShapeFn([](shape_inference::InferenceContext* c) {
      c->set_output(0, c->Scalar());
      DataType t;
      TF_RETURN_IF_ERROR(c->GetAttr("dtype", &t));
      PartialTensorShape p;
      TF_RETURN_IF_ERROR(c->GetAttr("shape", &p));
      shape_inference::ShapeHandle s;
      TF_RETURN_IF_ERROR(c->MakeShapeFromPartialTensorShape(p, &s));
      c->set_output_handle_shapes_and_types(

import types
from enum import Enum
from typing import Any, Callable, Dict, Set, Type, TypeVar, Union

from pydantic import BaseModel

DecoratedCallable = TypeVar("DecoratedCallable", bound=Callable[..., Any])
UnionType = getattr(types, "UnionType", Union)
ModelNameMap = Dict[Union[Type[BaseModel], Type[Enum]], str]

    OP_REQUIRES_OK(context,
                   context->GetAttr("output_file_path", &output_file_path));
    OP_REQUIRES_OK(context, context->env()->NewWritableFile(output_file_path,
                                                            &output_file_));

    OP_REQUIRES_OK(context, context->GetAttr("ids", &ids_));
    OP_REQUIRES_OK(context, context->GetAttr("calibration_methods",

from typing import Any, List, Union

import peewee
from pydantic import BaseModel
from pydantic.utils import GetterDict


class PeeweeGetterDict(GetterDict):
    def get(self, key: Any, default: Any = None):
        res = getattr(self._obj, key, default)
        if isinstance(res, peewee.ModelSelect):
            return list(res)
        return res


class ItemBase(BaseModel):
    title: str
    description: Union[str, None] = None

  LogicalResult matchAndRewrite(TF::CustomAggregatorOp op,
                                PatternRewriter &rewriter) const override {
    FloatAttr min = mlir::dyn_cast_or_null<FloatAttr>(op->getAttr("min"));
    FloatAttr max = mlir::dyn_cast_or_null<FloatAttr>(op->getAttr("max"));

    // When there are no min and max attributes, remove op.
    if (min == nullptr || max == nullptr) {
      op.getOutput().replaceAllUsesWith(op.getInput());