How to use the Tensor::accessor

Reading the documentation https://pytorch.org/cppdocs/notes/tensor_basics.html it looks like accessor() takes a template value which specifies the dimensionality. Is this value allowed to be different than the source tensor? For example if I want to access a tensor as a logical 1-d array, but the source tensor is a possibly noncontiguous (coming from a slice operation) n-d tensor, am I allowed to do my_tensor.accessor<float, 1>()?

I think I found the answer from the generated source code Tensor.h


  // Return a `TensorAccessor` for CPU `Tensor`s. You have to specify scalar type and
  // dimension.
  template<typename T, size_t N>
  TensorAccessor<T,N> accessor() const& {
    static_assert(N > 0, "accessor is used for indexing tensor, for scalars use *data<T>()");
    TORCH_CHECK(dim() == N, "expected ", N, " dims but tensor has ", dim());
    return TensorAccessor<T,N>(data<T>(),sizes().data(),strides().data());
  }