Function func_assert_same_pos

torch/_numpy/testing/utils.py:576–614

Handling nan/inf. Combine results of running func on x and y, checking that they are True at the same locations.

(x, y, func=isnan, hasval="nan")

    ox, oy = x, y

    def func_assert_same_pos(x, y, func=isnan, hasval="nan"):
        """Handling nan/inf.

        Combine results of running func on x and y, checking that they are True
        at the same locations.

        """
        __tracebackhide__ = True  # Hide traceback for py.test
        x_id = func(x)
        y_id = func(y)
        # We include work-arounds here to handle three types of slightly
        # pathological ndarray subclasses:
        # (1) all() on `masked` array scalars can return masked arrays, so we
        #     use != True
        # (2) __eq__ on some ndarray subclasses returns Python booleans
        #     instead of element-wise comparisons, so we cast to bool_() and
        #     use isinstance(..., bool) checks
        # (3) subclasses with bare-bones __array_function__ implementations may
        #     not implement np.all(), so favor using the .all() method
        # We are not committed to supporting such subclasses, but it's nice to
        # support them if possible.
        if (x_id == y_id).all().item() is not True:
            msg = build_err_msg(
                [x, y],
                err_msg + "\nx and y %s location mismatch:" % (hasval),
                verbose=verbose,
                header=header,
                names=("x", "y"),
                precision=precision,
            )
            raise AssertionError(msg)
        # If there is a scalar, then here we know the array has the same
        # flag as it everywhere, so we should return the scalar flag.
        if isinstance(x_id, bool) or x_id.ndim == 0:
            return bool_(x_id)
        elif isinstance(y_id, bool) or y_id.ndim == 0:
            return bool_(y_id)
        else:
            return y_id

    try:
        if strict: