Allow plans to have fewer dimensions than the data

I think it's a good idea to allow FFT plans to be applied to data that has more dimensions than the plan, as long as those are only the trailing dimensions. The expected behaviour in this case would be a batched FFT.
CUDA.jl PR https://github.com/JuliaGPU/CUDA.jl/pull/1903 initially implemented this behaviour for CUFFT, but it was removed because it is not in accordance with AbstractFFTs.

As an example
```
using CUDA
A = CUDA.rand(100); B = CUDA.rand(100,10);
plan = CUDA.CUFFT.plan_fft(A)
plan * A # works
plan * B # doesn't work
```
In this case the expected behaviour would be `(plan * B)[:,i] == (plan * B[:,i])`.

You'd only need a single plan to apply the plan to data in different batch sizes as well. E.g. in the following situation:
```
A = rand(100,10); B = rand(100,20); C = rand(100)
```
I'd like to have a plan that performs an FFT along the first dimension, but can apply to both `A` and `B`. It's a very common setup e.g. in ML applications, that the batch size is variable and not set when allocating the model.