Add support for Torch's Conv1d strides and ConvTranspose1d

[fcaspe]

Hi Jatin!

This library is really awesome, I have been using it for low latency inference of big convolutional autoencoders, so I have implemented the 1d Transposed Convolution and convolutional strides.

• ConvTranspose1d is implemented with RTNeural's Conv1D class, but a different loading function has to be called, RTNeural::torch_helpers::loadConvTranspose1D. See torch_convtranspose1d_test.cpp for an example.

• Conv1d strides are implemented using a .skip() method that performs a single stride step. This just updates the circular buffer of the Conv1D layer with the new input we jump over. For example, if strides=2 is required, then .skip() has to be called every time after a .forward() call is made. See torch_conv1d_stride_test.cpp for an example.

I know these new functionalities are not fully incorporated into the library. For instance, strides are still missing in Conv1DT and the non-streaming versions of Conv1D and Conv2D. Let me know what you think about these additions and I will be happy to improve them so that hopefully they can be integrated into the library!

Best,
Franco