* Bind cauchy_, exponential_, normal_, uniform_ functions to THPVariable.
Also changes the error messages around Generator parser; previously, you'd get an error
like: torch._C.Generator is not a torch.Generator; now the check is proper but returns
that only None is supported.
* Support passing Generators to ATen Variable-bound methods.
This involves changing THPGenerator to have an at::Generator rather than a THGenerator.
TH getRNGState, setRNGState are still called directly because they are not bound from ATen yet;
they should probably be on the Generators and return (opaque) GenerateState objects.
* Fix default values.
* Properly use THRandom_initialSeed.
* update standard gamma to use new default generator.
This adds some generated autograd functions implemented in C++, which
are generated from derivatives.yaml. It also generates Python bindings
for the Variable methods. The generated files are:
Functions.cpp/h: subclasses of torch::autograd::Function
VariableType.cpp/h: The at::Type for autograd Variables
python_variable_methods.cpp: Python bindings to torch::autograd::Variable
python_variable_methods_dispatch.h: wrapper which releases GIL and sets the
CUDA device
python_functions.cpp/h: exposes generated autograd functions as Python
objects
The generated functions are mostly shadowed by the definitions in
variable.py. We'll remove the Python implementations in favor of the
generated C++ implementations in a subsequent commit.
