Convis

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- GanglionSpiking had a bug in the refractory period
- the different implementations of OPL Layers now produce roughly the same output
- FullConvolutionOPLFilter now creates a filter from configuration options
- `Layer.run()` now always returns an `Output` object
+ fixed a bug where it returned an `Output` object wrapped in a `Output` object
- `Layer.run()` can now process infinite streams if it gets an argument `max_t`
- added warnings to `convis.streams` classes that are unstable
- fixed `convis.streams.ImageSequence`
- `utils.plot_tensor` is now available as `convis.plot_tensor`

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A small amount of bug fixes:

- Fixed a bug in `convis.filters.retina.GanglionInput`:
- the convolution filters now initialize without having to reapply a configuration to the layer/parameters
- made padding flags in `convis.filters.Conv3d` default to True
- in contrast to PyTorch `Conv3d` the output will now have the same shape as the input *by default*. To disable, use `time_pad=False` and `autopad=False` as arguments.
- fixed a bug when calling `.array()` on `convis.base.Output`s.
- fixed a bug where `resolution` was not a valid argument to `convis.filters.Conv3d.gaussian`

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- It is now possible to disable the computational graph.
- some issues with creating inline plots in Python 3 were fixed

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- fixed compatibility with PyTorch 0.4.0
+ `convis` is now compatible with PyTorch <= 0.3 and PyTorch 0.4.0
- VirtualParameters are now also included in the `named_parameter` dictionary and also appear in the `model.p.<tab>` parameter list.
- the `variables` submodule now offers the functions `zeros`, `zeros_like`, `ones`, `rand` and `randn` which give a autograd.Variable or a Tensor, depending on PyTorch version.