How to use the hypothesis.strategies.lists function in hypothesis

    @given(st.lists(st.binary(min_size=1024, max_size=1024), average_size=100))
    @settings(database=None, buffer_size=1000)
    def test(x):
        pass

def write_pattern(draw, min_size=0):
    keys = draw(st.lists(st.integers(0, 1000), unique=True, min_size=1))
    values = draw(st.lists(st.integers(), unique=True, min_size=1))
    return draw(
        st.lists(st.tuples(st.sampled_from(keys), st.sampled_from(values)),
                 min_size=min_size))

def test_can_find_nans():
    x = find(lists(floats()), lambda x: math.isnan(sum(x)))
    if len(x) == 1:
        assert math.isnan(x[0])
    else:
        assert 2 <= len(x) <= 3

def two_lists_equal_size(draw):
    list_one = draw(st.lists(float_st, min_size=1))
    size = len(list_one)
    list_two = draw(st.lists(float_st_nz, min_size=size, max_size=size))
    return list_one, list_two

@given(st.lists(st.integers(), min_size=1))
def test_smallest_random(values):
    int_heap = IntHeap()
    for v in values:
        heap.push(int_heap, v)

    target = random.choice(int_heap.values)
    valid = [i for (i, v) in enumerate(int_heap.values) if v == target]
    assert heap.smallest(int_heap, (lambda x: x == target)) in valid

        st.lists(concave_terms(), min_size=1),
    )
    def test_unknown(self, a, b):
        terms = a + b
        for c, _ in terms:
            assume(c != 0.0)
        cvx = self._rule_result(terms)
        assert cvx.is_unknown()

lambda children: st.builds(
            lambda x: encode_octet_string(x), st.one_of(children)
        )
        | st.builds(lambda x: encode_bitstring(x, 0), st.one_of(children))
        | st.builds(
            lambda x: encode_sequence(*x), st.lists(children, max_size=200)
        )
        | st.builds(
            lambda tag, x: encode_constructed(tag, x),
            st.integers(min_value=0, max_value=0x3F),
            st.one_of(children),
        ),

import unittest
from hypothesis import given
import hypothesis.strategies as st
from hypothesis.extra.datetime import dates
from arcas.PLOS.main import Plos

plos_entry = st.fixed_dictionaries(
                   {'id': st.text(min_size=5, max_size=5),
                    'journal': st.text(min_size=5, max_size=5),
                    'article_type': st.text(min_size=5, max_size=5),
                    'author_display': st.lists(elements=st.text(min_size=5,
                                                                max_size=5),
                                               min_size=5, max_size=5,
                                               unique=True),
                    'abstract': st.text(min_size=5, max_size=5),
                    'title_display': st.text(min_size=5, max_size=5),
                    'score': st.text(min_size=5, max_size=5)
                    })

dummy_arguments = st.fixed_dictionaries(
                    {'-a': st.text(min_size=5, max_size=5),
                     '-t': st.text(min_size=5, max_size=5),
                     '-b': st.text(min_size=5, max_size=5),
                     '-y': st.text(min_size=5, max_size=5),
                     '-r': st.text(min_size=5, max_size=5),
                     '-s': st.text(min_size=5, max_size=5)
                     })

            metrics=st.lists(st.text(), min_size=2).filter(
                lambda x: len(set(x)) != len(x)
            )
        ),
        dict(
            metrics=cst.everything_except((str, Sequence))
            | st.lists(cst.everything_except(str))
        ),
        dict(
            nrows=cst.everything_except((Integral, type(None)))
            | st.integers(max_value=0)
        ),
        dict(
            ncols=cst.everything_except((Integral, type(None)))
            | st.integers(max_value=0)
        ),
        dict(

lambda x: one_of(
        lists(x, max_size=2),
        tuples(x),
        dictionaries(unqualified_identifiers, x, max_size=2),
    ).map(py.Literal),
    max_leaves=8,