module would be initialized on construction and the first module
   would be initialized during the first dry run. This can cause the
   parameters of a network using lazy modules to be initialized
   differently than the parameters of a network without lazy modules
   as the order of parameter initializations, which often depends on a
   stateful random number generator, is different. Check
   Reproducibility for more details.
Lazy modules can be serialized with a state dict like other
   modules. For example:



lazy_mlp = LazyMLP()
The state dict shows the uninitialized parameters
lazy_mlp.state_dict()
   OrderedDict([('fc1.weight', Uninitialized parameter),
                ('fc1.bias',
                 tensor([-1.8832e+25,  4.5636e-41, -1.8832e+25,  4.5636e-41, -6.1598e-30,
                          4.5637e-41, -1.8788e+22,  4.5636e-41, -2.0042e-31,  4.5637e-41])),
                ('fc2.weight', Uninitialized parameter),
                ('fc2.bias', tensor([0.0019]))])


