class User(Base):
    __tablename__ = "users"

    id = Column(Integer, primary_key=True)
    email = Column(String, unique=True, index=True)
    hashed_password = Column(String)
    is_active = Column(Boolean, default=True)

    items = relationship("Item", back_populates="owner")


class Item(Base):
    __tablename__ = "items"

    id = Column(Integer, primary_key=True)
    title = Column(String, index=True)

class User(Base):
    __tablename__ = "users"

    id = Column(Integer, primary_key=True)
    email = Column(String, unique=True, index=True)
    hashed_password = Column(String)
    is_active = Column(Boolean, default=True)

    items = relationship("Item", back_populates="owner")


class Item(Base):
    __tablename__ = "items"

    id = Column(Integer, primary_key=True)
    title = Column(String, index=True)

class User(Base):
    __tablename__ = "users"

    id = Column(Integer, primary_key=True)
    email = Column(String, unique=True, index=True)
    hashed_password = Column(String)
    is_active = Column(Boolean, default=True)

    items = relationship("Item", back_populates="owner")


class Item(Base):
    __tablename__ = "items"

    id = Column(Integer, primary_key=True)
    title = Column(String, index=True)

database = databases.Database(DATABASE_URL)

metadata = sqlalchemy.MetaData()

notes = sqlalchemy.Table(
    "notes",
    metadata,
    sqlalchemy.Column("id", sqlalchemy.Integer, primary_key=True),
    sqlalchemy.Column("text", sqlalchemy.String),
    sqlalchemy.Column("completed", sqlalchemy.Boolean),
)


engine = sqlalchemy.create_engine(
    DATABASE_URL, connect_args={"check_same_thread": False}

    assert response.status_code == status.HTTP_200_OK
    actual_schema = response.json()
    assert actual_schema == schema
    assert (
        len(actual_schema["paths"]["/"]["get"]["parameters"]) == 1
    )  # primary goal of this test


def test_response():
    response = client.get("/", headers={"someheader": "hello"})
    assert response.status_code == status.HTTP_200_OK

"""
Top-level module of TensorFlow. By convention, we refer to this module as
`tf` instead of `tensorflow`, following the common practice of importing
TensorFlow via the command `import tensorflow as tf`.

The primary function of this module is to import all of the public TensorFlow
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.