('fc2.bias', tensor([0.0019]))])
Lazy modules can load regular "torch.nn.Parameter" s (i.e. you can
   serialize/deserialize initialized LazyModules and they will remain
   initialized)



full_mlp = LazyMLP()
Dry run to initialize another module
full_mlp.forward(torch.ones(10, 1))
Load an initialized state into a lazy module
lazy_mlp.load_state_dict(full_mlp.state_dict())
The state dict now holds valid values
lazy_mlp.state_dict()
   OrderedDict([('fc1.weight',
                 tensor([[-0.3837],
                         [ 0.0907],
                         [ 0.6708],
                         [-0.5223],
                         [-0.9028],
                         [ 0.2851],
                         [-0.4537],
                         [ 0.6813],
                         [ 0.5766],
                         [-0.8678]])),
                ('fc1.bias',
                 tensor([-1.8832e+25,  4.5636e-41, -1.8832e+25,  4.5636e-41, -6.1598e-30,


