Add ability to omit certain fields from a model when converting to a dict #228

Is your feature request related to a problem? Please describe.
I have an endpoint which has optional fields in the body where optional means "can be excluded" instead of "can be null" (where these have different behavior e.g. null means set to null vs. not included means "don't change"). When using a generated client to hit the endpoint, optional fields that I don't set are sent up as None, causing the API to interpret it as "update this value to equal None" instead of "I'm not changing this value so leave it alone". It'd be nice to have a way to specify fields to include/exclude when making a model and/or when using it, ideally with options similar to pydantic's when exporting a schema to a dictionary

Describe the solution you'd like
At a minimum, I'd like to be able to specify the following in a model's .to_dict():

• include - a set of field names to include in the dictionary
• exclude - a set of field names to exclude from the dictionary (only one of this and include should be used at once)
• exclude_unset - if True, values that aren't explicitly set are excluded. This one might be tricky as I don't think attrs classes keep track of what has or hasn't been set
• exclude_none - a nice catch-all for excluding any value set to None.

Once these parameters are available in models' .to_dict() there would need to be a way to specify what params to use when calling a model's .to_dict() in an endpoint's _get_kwargs. I have two ideas for how this could be done:=

• All model's inherit from a BaseModel that has an attribute called something like model_config or to_dict_config that can be set on a per-instance basis i.e. my_model.to_dict_config = {"exclude": {"owner_pk", "location_pk"}}
• Implement the above params as methods on a BaseModel class (or per model but thats a tad yucky) to use them as a fluid API i.e. my_model = MyModel(**data).include({"thing1","thing2"}).exclude_none(True) etc.

Describe alternatives you've considered
An alternative would be to just convert all generated models to be pydantic schemas, thus allowing us to leverage the handy options in its .dict().