Currently I am using hypothesis fixed_dictionaries strategy to generate a dictionary with specific keys and data types that are considered valid for my application. I need a strategy which produces this fixed dictionary as well as others with specific keys removed. Or a dictionary with a certain minimal set of keys with optional additional ones, preferably in a way that produces the various combinations of these optional keys.
This is an example of the json schema that needs to be validated, with the 2 optional fields. I'd like to generate all possible valid data for this schema.
'user_stub': {
    '_id':        {'type': 'string'},
    'username':   {'type': 'string'},
    'social':     {'type': 'string'},
    'api_name':   {'type':     'string',
                   'required': False},
    'profile_id': {'type':     'integer',
                   'required': False},
}
This is what I came up with but it is incorrect because it retains the keys but uses None as the value, and I want instead that the keys are removed.
return st.fixed_dictionaries({
    '_id':        st.text(),
    'username':   st.text(),
    'social':     st.text(),
    'api_name':   st.one_of(st.none(),
                            st.text()),
    'profile_id': st.one_of(st.none(),
                            st.integers()),
})
EDIT: updated composite strategy ->
Seems like it would be best to separate the additional optional dictionaries based on the type of data being returned, otherwise might get keys with mismatched values.
@st.composite
def generate_data(draw):
    base_data = st.fixed_dictionaries({
        '_id':      st.text(),
        'username': st.text(),
        'social':   st.text(),
    })
    optional_strs = st.dictionaries(
        keys=st.just('api_name'),
        values=st.text()
    )
    optional_ints = st.dictionaries(
        keys=st.just('profile_id'),
        values=st.integers()
    )
    b = draw(base_data)
    s = draw(optional_strs)
    i = draw(optional_ints)
    return {**b, **s, **i}  # noice
 
     
    