engine = create_engine(sqlite_url, connect_args=connect_args)


def create_db_and_tables():
    SQLModel.metadata.create_all(engine)


def get_session():
    with Session(engine) as session:
        yield session


SessionDep = Annotated[Session, Depends(get_session)]

app = FastAPI()


@app.on_event("startup")
def on_startup():
    create_db_and_tables()


@app.post("/heroes/")

    }

    @Override
    protected Session newSession(RepositorySystemSession repoSession, List<RemoteRepository> repositories) {
        MavenSession t = getMavenSession();
        final MavenSession ms = requireNonNull(t);
        final MavenSession mss;
        if (repoSession != ms.getRepositorySession()) {
            mss = new MavenSession(repoSession, ms.getRequest(), ms.getResult());
        } else {
            mss = ms;

#!/usr/bin/env python
# -*- coding: utf-8 -*-

import logging

import boto3
from boto3.session import Session
from botocore.session import get_session

from client_grants import ClientGrantsCredentialProvider

boto3.set_stream_logger('boto3.resources', logging.DEBUG)

bc_session = get_session()
bc_session.get_component('credential_provider').insert_before(
    'env',
    ClientGrantsCredentialProvider('NZLOOFRSluw9RfIkuHGqfk1HFp4a',

class User(SQLModel, table=True):
    id: int | None = Field(default=None, primary_key=True)
    name: str


app = FastAPI()


def get_session():
    with Session(engine) as session:
        yield session


def get_user(user_id: int, session: Annotated[Session, Depends(get_session)]):
    user = session.get(User, user_id)
    if not user:
        raise HTTPException(status_code=403, detail="Not authorized")

engine = create_engine(sqlite_url, connect_args=connect_args)


def create_db_and_tables():
    SQLModel.metadata.create_all(engine)


def get_session():
    with Session(engine) as session:
        yield session


SessionDep = Annotated[Session, Depends(get_session)]
app = FastAPI()


@app.on_event("startup")
def on_startup():
    create_db_and_tables()

        return container;
    }

    @BeforeEach
    public void setUp() throws Exception {
        RepositorySystemSession repoSession = initRepoSession();
        MavenSession session = new MavenSession(
                getContainer(), repoSession, new DefaultMavenExecutionRequest(), new DefaultMavenExecutionResult());
        session.setSession(new DefaultSessionFactory(

  Session* session;
  status->status = NewSession(opt->options, &session);
  if (status->status.ok()) {
    TF_Session* new_session = new TF_Session(session, graph);
    if (graph != nullptr) {
      mutex_lock l(graph->mu);
      graph->sessions[new_session] = "";
    }
    return new_session;
  } else {
    LOG(ERROR) << status->status;
    DCHECK_EQ(nullptr, session);
    return nullptr;
  }
}

    *   symbolic tensors outside the scope of the model are used in custom loss
        functions. The flag can be disabled for these cases and ideally the
        usage pattern will need to be fixed.
    *   Mark Keras `set_session` as `compat.v1` only.
    *   `tf.keras.estimator.model_to_estimator` now supports exporting to
        `tf.train.Checkpoint format`, which allows the saved checkpoints to be
        compatible with `model.load_weights`.