continue
        param_schema = get_schema_from_model_field(
            field=param,
            schema_generator=schema_generator,
            model_name_map=model_name_map,
            field_mapping=field_mapping,
            separate_input_output_schemas=separate_input_output_schemas,
        )
        parameter = {
            "name": param.alias,
            "in": field_info.in_.value,

import inspect
from contextlib import AsyncExitStack, contextmanager
from copy import copy, deepcopy
from typing import (
    Any,
    Callable,
    Coroutine,
    Dict,
    ForwardRef,
    List,
    Mapping,
    Optional,
    Sequence,
    Tuple,
    Type,
    Union,
    cast,
)

import anyio
from fastapi import params
from fastapi._compat import (
    PYDANTIC_V2,
    ErrorWrapper,
    ModelField,

    variables: Optional[Dict[str, ServerVariable]] = None


class Reference(BaseModel):
    ref: str = Field(alias="$ref")


class Discriminator(BaseModel):
    propertyName: str
    mapping: Optional[Dict[str, str]] = None


class XML(BaseModelWithConfig):
    name: Optional[str] = None
    namespace: Optional[str] = None
    prefix: Optional[str] = None
    attribute: Optional[bool] = None

        inputs = [
            (field, override_mode or field.mode, field._type_adapter.core_schema)
            for field in fields
        ]
        field_mapping, definitions = schema_generator.generate_definitions(
            inputs=inputs
        )
        return field_mapping, definitions  # type: ignore[return-value]

    def is_scalar_field(field: ModelField) -> bool:
        from fastapi import params

    This is useful when we use ConstrainedDecimal to represent Numeric(x,0)
    where a integer (but not int typed) is used. Encoding this as a float
    results in failed round-tripping between encode and parse.
    Our Id type is a prime example of this.

    >>> decimal_encoder(Decimal("1.0"))
    1.0

    >>> decimal_encoder(Decimal("1"))
    1
    """