Add source inversion capability

[ar4]

At the moment Deepwave only calculates the gradient of the loss with respect
to the model, but for real data the source wavelet is also often not well
known and must be inverted for. I think it should be possible to add this
capability to Deepwave.

deepwave/scalar/scalar.py will need to be modified to allocate memory to store
this gradient, and to pass this memory and the locations of the sources to
backpropagation, possibly with a flag specifying whether the source gradient
should be calculated (which will depend on whether source_amplitudes has
requires_grad set to True), and also to return the calculated gradient as an
output (replacing the appropriate None value in the list of outputs).

deepwave/scalar/scalar.c and scalar.h will need to be modified to accept the
new inputs into the backward function, and to calculate the gradient.