from fastapi.dependencies.utils import (
    multipart_incorrect_install_error,
    multipart_not_installed_error,
)


def test_incorrect_multipart_installed_form(monkeypatch):
    monkeypatch.setattr("python_multipart.__version__", "0.0.12")
    with warnings.catch_warnings(record=True):
        warnings.simplefilter("always")
        monkeypatch.delattr("multipart.multipart.parse_options_header", raising=False)

_initializers = _KerasLazyLoader(
    globals(), submodule="initializers", name="initializers")
setattr(_current_module, "losses", _losses)
setattr(_current_module, "metrics", _metrics)
setattr(_current_module, "optimizers", _optimizers)
setattr(_current_module, "initializers", _initializers)


# Do an eager load for Keras' code so that any function/method that needs to

    }

    public void setId(final String id) {
        this.id = id;
    }

    public void setCss(final String css) {
        this.css = css;
    }

    public void setAttr(final String name, final String value) {
        attrName = name;
        attrValue = value;
    }

    @Override
    public String toString() {

    }

    public void test_toString() {
        String tag = "tag", id = "id", css = "css", attrName = "attrName", attrValue = "attrValue";
        PrunedTag prunedtag = new PrunedTag(tag);
        prunedtag.setAttr(attrName, attrValue);
        prunedtag.setId(id);
        prunedtag.setCss(css);
        assertEquals("PrunedTag [tag=" + tag + ", id=" + id + ", css=" + css + ", attrName=" + attrName + ", attrValue=" + attrValue + "]",

  import tensorflow_io_gcs_filesystem as _tensorflow_io_gcs_filesystem

# Lazy-load Keras v1.
_tf_uses_legacy_keras = (
    _os.environ.get("TF_USE_LEGACY_KERAS", None) in ("true", "True", "1"))
setattr(_current_module, "keras", _KerasLazyLoader(globals(), mode="v1"))
_module_dir = _module_util.get_parent_dir_for_name("keras._tf_keras.keras")
_current_module.__path__ = [_module_dir] + _current_module.__path__

                    # For Pydantic v1
                    and getattr(field, "shape", 1) == 1
                )
            )

    return ParamDetails(type_annotation=type_annotation, depends=depends, field=field)


def add_param_to_fields(*, field: ModelField, dependant: Dependant) -> None:
    field_info = field.field_info
    field_info_in = getattr(field_info, "in_", None)
    if field_info_in == params.ParamTypes.path:

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]

            openapi_examples = getattr(field_info, "openapi_examples", None)
            example = getattr(field_info, "example", None)
            if openapi_examples:
                parameter["examples"] = jsonable_encoder(openapi_examples)
            elif example != Undefined:
                parameter["example"] = jsonable_encoder(example)
            if getattr(field_info, "deprecated", None):

            throw new UncheckedIOException(e);
        }
    }

    /**
     * Automatically accept changes that are valid property upgrades of a getter or setter.
     *
     * Here we automatically accept the following cases:
     * - A setter `setX` of an upgraded property is removed
     * - A boolean `isX` of an upgraded property is removed

	if err != nil {
		return 0, err
	}

	return binary.LittleEndian.Uint64(buf[:8]), nil
}

func listxattr(path string) ([]string, error) {
	return xattr.LList(path)
}

func setxattr(path, name string, value uint64) error {
	data := make([]byte, 8)
	binary.LittleEndian.PutUint64(data, value)
	return xattr.LSet(path, name, data)
}

func main() {