# Buggy code
def value_counts(
    self, normalize=False, sort=True, ascending=False, bins=None, dropna=True
):
    from pandas.core.reshape.merge import get_join_indexers
    from pandas.core.reshape.tile import cut

    ids, _, _ = self.grouper.group_info
    val = self.obj._values

    def apply_series_value_counts():
        return self.apply(
            Series.value_counts,
            normalize=normalize,
            sort=sort,
            ascending=ascending,
            bins=bins,
        )

    if bins is not None:
        if not np.iterable(bins):
            # scalar bins cannot be done at top level
            # in a backward compatible way
            return apply_series_value_counts()
    elif is_categorical_dtype(val):
        # GH38672
        return apply_series_value_counts()

    # groupby removes null keys from groupings
    mask = ids != -1
    ids, val = ids[mask], val[mask]

    if bins is None:
        lab, lev = algorithms.factorize(val, sort=True)
        llab = lambda lab, inc: lab[inc]
    else:
        # lab is a Categorical with categories an IntervalIndex
        lab = cut(Series(val), bins, include_lowest=True)
        lev = lab.cat.categories
        lab = lev.take(lab.cat.codes, allow_fill=True, fill_value=lev._na_value)
        llab = lambda lab, inc: lab[inc]._multiindex.codes[-1]

    if is_interval_dtype(lab.dtype):
        # TODO: should we do this inside II?
        sorter = np.lexsort((lab.left, lab.right, ids))
    else:
        sorter = np.lexsort((lab, ids))

    ids, lab = ids[sorter], lab[sorter]

    # group boundaries are where group ids change
    idx = np.r_[0, 1 + np.nonzero(ids[1:] != ids[:-1])[0]]

    # new values are where sorted labels change
    lchanges = llab(lab, slice(1, None)) != llab(lab, slice(None, -1))
    inc = np.r_[True, lchanges]
    inc[idx] = True  # group boundaries are also new values
    out = np.diff(np.nonzero(np.r_[inc, True])[0])  # value counts

    # num. of times each group should be repeated
    rep = partial(np.repeat, repeats=np.add.reduceat(inc, idx))

    # multi-index components
    codes = self.grouper.reconstructed_codes
    codes = [rep(level_codes) for level_codes in codes] + [llab(lab, inc)]
    levels = [ping.group_index for ping in self.grouper.groupings] + [lev]
    names = self.grouper.names + [self._selection_name]

    if dropna:
        mask = codes[-1] != -1
        if mask.all():
            dropna = False
        else:
            out, codes = out[mask], [level_codes[mask] for level_codes in codes]

    if normalize:
        out = out.astype("float")
        d = np.diff(np.r_[idx, len(ids)])
        if dropna:
            m = ids[lab == -1]
            np.add.at(d, m, -1)
            acc = rep(d)[mask]
        else:
            acc = rep(d)
        out /= acc

    if sort and bins is None:
        cat = ids[inc][mask] if dropna else ids[inc]
        sorter = np.lexsort((out if ascending else -out, cat))
        out, codes[-1] = out[sorter], codes[-1][sorter]

    if bins is None:
        mi = MultiIndex(levels=levels, codes=codes, names=names, verify_integrity=False)

        if is_integer_dtype(out):
            out = ensure_int64(out)
        return self.obj._constructor(out, index=mi, name=self._selection_name)

    # for compat. with libgroupby.value_counts need to ensure every
    # bin is present at every index level, null filled with zeros
    diff = np.zeros(len(out), dtype="bool")
    for level_codes in codes[:-1]:
        diff |= np.r_[True, level_codes[1:] != level_codes[:-1]]

    ncat, nbin = diff.sum(), len(levels[-1])

    left = [np.repeat(np.arange(ncat), nbin), np.tile(np.arange(nbin), ncat)]

    right = [diff.cumsum() - 1, codes[-1]]

    _, idx = get_join_indexers(left, right, sort=False, how="left")
    out = np.where(idx != -1, out[idx], 0)

    if sort:
        sorter = np.lexsort((out if ascending else -out, left[0]))
        out, left[-1] = out[sorter], left[-1][sorter]

    # build the multi-index w/ full levels
    def build_codes(lev_codes: np.ndarray) -> np.ndarray:
        return np.repeat(lev_codes[diff], nbin)

    codes = [build_codes(lev_codes) for lev_codes in codes[:-1]]
    codes.append(left[-1])

    mi = MultiIndex(levels=levels, codes=codes, names=names, verify_integrity=False)

    if is_integer_dtype(out):
        out = ensure_int64(out)
    return self.obj._constructor(out, index=mi, name=self._selection_name)


# Traceback (IndexError)
Traceback (most recent call last):
File "<input>", line 1, in <module>
File "/Users/username/.virtualenvs/my_project/lib/python3.8/site-packages/pandas/core/groupby/generic.py", line 736, in value_counts
codes = [rep(level_codes) for level_codes in codes] + [llab(lab, inc)]
File "/Users/username/.virtualenvs/my_project/lib/python3.8/site-packages/pandas/core/groupby/generic.py", line 705, in <lambda>
llab = lambda lab, inc: lab[inc]
IndexError: boolean index did not match indexed array along dimension 0; dimension is 0 but corresponding boolean dimension is 1

# Fixed code
def value_counts(
    self, normalize=False, sort=True, ascending=False, bins=None, dropna=True
):
    from pandas.core.reshape.merge import get_join_indexers
    from pandas.core.reshape.tile import cut

    ids, _, _ = self.grouper.group_info
    val = self.obj._values

    def apply_series_value_counts():
        return self.apply(
            Series.value_counts,
            normalize=normalize,
            sort=sort,
            ascending=ascending,
            bins=bins,
        )

    if bins is not None:
        if not np.iterable(bins):
            # scalar bins cannot be done at top level
            # in a backward compatible way
            return apply_series_value_counts()
    elif is_categorical_dtype(val):
        # GH38672
        return apply_series_value_counts()

    # groupby removes null keys from groupings
    mask = ids != -1
    ids, val = ids[mask], val[mask]

    if bins is None:
        lab, lev = algorithms.factorize(val, sort=True)
        llab = lambda lab, inc: lab[inc]
    else:
        # lab is a Categorical with categories an IntervalIndex
        lab = cut(Series(val), bins, include_lowest=True)
        lev = lab.cat.categories
        lab = lev.take(lab.cat.codes, allow_fill=True, fill_value=lev._na_value)
        llab = lambda lab, inc: lab[inc]._multiindex.codes[-1]

    if is_interval_dtype(lab.dtype):
        # TODO: should we do this inside II?
        sorter = np.lexsort((lab.left, lab.right, ids))
    else:
        sorter = np.lexsort((lab, ids))

    ids, lab = ids[sorter], lab[sorter]

    # group boundaries are where group ids change
    idchanges = 1 + np.nonzero(ids[1:] != ids[:-1])[0]
    idx = np.r_[0, idchanges]
    if not len(ids):
        idx = idchanges

    # new values are where sorted labels change
    lchanges = llab(lab, slice(1, None)) != llab(lab, slice(None, -1))
    inc = np.r_[True, lchanges]
    if not len(lchanges):
        inc = lchanges
    inc[idx] = True  # group boundaries are also new values
    out = np.diff(np.nonzero(np.r_[inc, True])[0])  # value counts

    # num. of times each group should be repeated
    rep = partial(np.repeat, repeats=np.add.reduceat(inc, idx))

    # multi-index components
    codes = self.grouper.reconstructed_codes
    codes = [rep(level_codes) for level_codes in codes] + [llab(lab, inc)]
    levels = [ping.group_index for ping in self.grouper.groupings] + [lev]
    names = self.grouper.names + [self._selection_name]

    if dropna:
        mask = codes[-1] != -1
        if mask.all():
            dropna = False
        else:
            out, codes = out[mask], [level_codes[mask] for level_codes in codes]

    if normalize:
        out = out.astype("float")
        d = np.diff(np.r_[idx, len(ids)])
        if dropna:
            m = ids[lab == -1]
            np.add.at(d, m, -1)
            acc = rep(d)[mask]
        else:
            acc = rep(d)
        out /= acc

    if sort and bins is None:
        cat = ids[inc][mask] if dropna else ids[inc]
        sorter = np.lexsort((out if ascending else -out, cat))
        out, codes[-1] = out[sorter], codes[-1][sorter]

    if bins is None:
        mi = MultiIndex(levels=levels, codes=codes, names=names, verify_integrity=False)

        if is_integer_dtype(out):
            out = ensure_int64(out)
        return self.obj._constructor(out, index=mi, name=self._selection_name)

    # for compat. with libgroupby.value_counts need to ensure every
    # bin is present at every index level, null filled with zeros
    diff = np.zeros(len(out), dtype="bool")
    for level_codes in codes[:-1]:
        diff |= np.r_[True, level_codes[1:] != level_codes[:-1]]

    ncat, nbin = diff.sum(), len(levels[-1])

    left = [np.repeat(np.arange(ncat), nbin), np.tile(np.arange(nbin), ncat)]

    right = [diff.cumsum() - 1, codes[-1]]

    _, idx = get_join_indexers(left, right, sort=False, how="left")
    out = np.where(idx != -1, out[idx], 0)

    if sort:
        sorter = np.lexsort((out if ascending else -out, left[0]))
        out, left[-1] = out[sorter], left[-1][sorter]

    # build the multi-index w/ full levels
    def build_codes(lev_codes: np.ndarray) -> np.ndarray:
        return np.repeat(lev_codes[diff], nbin)

    codes = [build_codes(lev_codes) for lev_codes in codes[:-1]]
    codes.append(left[-1])

    mi = MultiIndex(levels=levels, codes=codes, names=names, verify_integrity=False)

    if is_integer_dtype(out):
        out = ensure_int64(out)
    return self.obj._constructor(out, index=mi, name=self._selection_name)