assert response.json() == IsDict(
        {
            "detail": [
                {
                    "type": "int_parsing",
                    "loc": ["query", "query"],
                    "msg": "Input should be a valid integer, unable to parse string as an integer",
                    "input": "42.5",
                }
            ]
        }
    ) | IsDict(
        # TODO: remove when deprecating Pydantic v1

    This is a dependency class to collect the `username` and `password` as form data
    for an OAuth2 password flow.

    The OAuth2 specification dictates that for a password flow the data should be
    collected using form data (instead of JSON) and that it should have the specific
    fields `username` and `password`.

    All the initialization parameters are extracted from the request.

    Read more about it in the

    assert response.json() == IsDict(
        {
            "detail": [
                {
                    "type": "float_parsing",
                    "loc": ["body", "price"],
                    "msg": "Input should be a valid number, unable to parse string as a number",
                    "input": "twenty",
                }
            ]
        }
    ) | IsDict(
        # TODO: remove when deprecating Pydantic v1

    assert response.json() == IsDict(
        {
            "detail": [
                {
                    "type": "float_parsing",
                    "loc": ["body", "price"],
                    "msg": "Input should be a valid number, unable to parse string as a number",
                    "input": "twenty",
                }
            ]
        }
    ) | IsDict(
        # TODO: remove when deprecating Pydantic v1

So, the URL for that file would be something like: `/files/home/johndoe/myfile.txt`.

### OpenAPI support

OpenAPI doesn't support a way to declare a *path parameter* to contain a *path* inside, as that could lead to scenarios that are difficult to test and define.

Nevertheless, you can still do it in **FastAPI**, using one of the internal tools from Starlette.

                    "loc": ["query", "skip"],
                    "msg": "Input should be a valid integer, unable to parse string as an integer",
                    "input": "a",
                },
                {
                    "type": "int_parsing",
                    "loc": ["query", "limit"],
                    "msg": "Input should be a valid integer, unable to parse string as an integer",
                    "input": "b",

You can import it directly from `fastapi`:

```python
from fastapi import Request
```

!!! tip
    When you want to define dependencies that should be compatible with both HTTP and WebSockets, you can define a parameter that takes an `HTTPConnection` instead of a `Request` or a `WebSocket`.

    * You wouldn't write an application in Uvicorn directly. That would mean that your code would have to include more or less, at least, all the code provided by Starlette (or **FastAPI**). And if you did that, your final application would have the same overhead as having used a framework and minimizing your app code and bugs.

    assert response.json() == IsDict(
        {
            "detail": [
                {
                    "type": "int_parsing",
                    "loc": ["body", "size"],
                    "msg": "Input should be a valid integer, unable to parse string as an integer",
                    "input": "XL",
                }
            ],
            "body": {"title": "towel", "size": "XL"},
        }
    ) | IsDict(

        {
            "detail": [
                {
                    "type": "string_pattern_mismatch",
                    "loc": ["query", "item-query"],
                    "msg": "String should match pattern '^fixedquery$'",
                    "input": "nonregexquery",
                    "ctx": {"pattern": "^fixedquery$"},
                }
            ]
        }
    ) | IsDict(