pydantic | Data validation using Python type hints

class Inputs(BaseModel):
    id: int
    f1: float
    f2: float
    f3: str

class InputsList(BaseModel):
    inputs: List[Inputs]

{
  "inputs": [
    {
      "id": 1,
      "f1": 1.0,
      "f2": 1.0,
      "f3":

class Inputs(BaseModel):
    id: int
    f1: float
    f2: float
    f3: str

{
  "inputs": [
    {
      "id": 1,
      "f1": 1.0,
      "f2": 1.0,
      "f3": "text"
    },
    {
      "id": 2,
      "f1": 2.0,
  

from pydantic import BaseModel, Extra

class Pet(BaseModel):
    name: str

    class Config:
        extra = Extra.forbid

data = {
    "name": "some name",
    "some_extra_field": "some value",
}

my_pet = Pet.parse_obj(data)   # <- e

class ModifiedBaseModel(BaseModel):
    def __init__(__pydantic_self__, **data: Any) -> None:
        registered, not_registered = __pydantic_self__.filter_data(data)
        super().__init__(**registered)
        for k, v in not_regist

class Seller(BaseModel):
    seller_id: int
    display_name: str
    fulfilled_by_vender: bool

    @validator("fulfilled_by_vender", pre=True)
    def verify_if_exists(cls, value):
        return 'Packed & shipped by Vender' in value

class Card(BaseModel):
    card_name: str
    price_updated_date: datetime.datetime,
    card_img_id: str
    set_name: str
    set_tag: str
    rarity: str
    price_normal: float  # or maybe better, decimal.Decimal  - depends on your use