"type": "string",
                            "format": "date-time",
                        },
                        "repeat_at": IsDict(
                            {
                                "title": "Repeat At",
                                "anyOf": [
                                    {"type": "string", "format": "time"},
                                    {"type": "null"},

# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,

def get_user(db, username: str):
    if username in db:
        user_dict = db[username]
        return UserInDB(**user_dict)


def fake_decode_token(token):
    # This doesn't provide any security at all
    # Check the next version
    user = get_user(fake_users_db, token)
    return user


async def get_current_user(token: Annotated[str, Depends(oauth2_scheme)]):
    user = fake_decode_token(token)

    process_after: timedelta = Body(),
    repeat_at: Union[time, None] = Body(default=None),
):
    start_process = start_datetime + process_after
    duration = end_datetime - start_process
    return {
        "item_id": item_id,
        "start_datetime": start_datetime,
        "end_datetime": end_datetime,
        "process_after": process_after,
        "repeat_at": repeat_at,
        "start_process": start_process,

                            "type": "string",
                            "format": "date-time",
                        },
                        "repeat_at": IsDict(
                            {
                                "title": "Repeat At",
                                "anyOf": [
                                    {"type": "string", "format": "time"},
                                    {"type": "null"},

# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,

    And this path operation will:

    * Send the invoice to the client.
    * Collect the money from the client.
    * Send a notification back to the API user (the external developer), as a callback.
        * At this point is that the API will somehow send a POST request to the
            external API with the notification of the invoice event
            (e.g. "payment successful").
    """

        self.name = name
        self.call = call
        self.use_cache = use_cache
        # Store the path to be able to re-generate a dependable from it in overrides
        self.path = path
        # Save the cache key at creation to optimize performance

    process_after: Annotated[timedelta, Body()],
    repeat_at: Annotated[time | None, Body()] = None,
):
    start_process = start_datetime + process_after
    duration = end_datetime - start_process
    return {
        "item_id": item_id,
        "start_datetime": start_datetime,
        "end_datetime": end_datetime,
        "process_after": process_after,
        "repeat_at": repeat_at,
        "start_process": start_process,

    repeat_at: Annotated[Union[time, None], Body()] = None,
):
    start_process = start_datetime + process_after
    duration = end_datetime - start_process
    return {
        "item_id": item_id,
        "start_datetime": start_datetime,
        "end_datetime": end_datetime,
        "process_after": process_after,
        "repeat_at": repeat_at,
        "start_process": start_process,