7391aec984c1e9e0899d51e6b0538483c37ec8fb,torchdiffeq/_impl/misc.py,,_check_inputs,#,134

Before Change

    tensor_input = False
    if torch.is_tensor(y0):
        tensor_input = True
        y0 = (y0,)
        _base_nontuple_func_ = func
        func = lambda t, y: (_base_nontuple_func_(t, y[0]),)
    assert isinstance(y0, tuple), "y0 must be either a torch.Tensor or a tuple"
    for y0_ in y0:
        assert torch.is_tensor(y0_), "each element must be a torch.Tensor but received {}".format(type(y0_))

    if _decreasing(t):
        t = -t
        _base_reverse_func = func
        func = lambda t, y: tuple(-f_ for f_ in _base_reverse_func(-t, y))

After Change

    if not torch.is_tensor(y0):
        assert isinstance(y0, tuple), "y0 must be either a torch.Tensor or a tuple"
        tensor_input = False
        shapes = [y0_.shape for y0_ in y0]
        y0 = torch.cat([y0_.reshape(-1) for y0_ in y0])
        func = _TupleFunc(func, shapes)
    if not torch.is_floating_point(y0):
        raise TypeError("`y0` must be a floating point Tensor but is a {}".format(y0.type()))

    if _decreasing(t):

Instances