s:`~torch.Tensor` into :class:`~torch.nn.Parameter`.

    Args:
        parameters (iterable, optional): an iterable of elements to add to the list.

    Example::

        class MyModule(nn.Module):
            def __init__(self):
                super().__init__()
                self.params = nn.ParameterList([nn.Parameter(torch.randn(10, 10)) for i in range(10)])

            def forward(self, x):
                # ParameterList can act as an iterable, or be indexed using ints
                for i, p in enumerate(self.params):
                    x = self.params[i // 2].mm(x) + p.mm(x)
                return x
    NrV