I have run this code:
z = torch.load(load_path)

for k, v in z.items():
    print(z)
model.load_state_dict(z)

..........('base_loss', tensor([[0.]], device='cuda:0')),
             ('b', tensor(5., device='cuda:0')),
             ('x_grid',
              tensor([[[[[-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
                          -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
                          -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
                          -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000],
                         [-0.9259, -0.9259, -0.9259, -0.9259, -0.9259, -0.9259, -0.9259,
                          -0.9259, -0.9259, -0.9259, -0.9259, -0.9259,.....


---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-11-954a267c40b0> in <module>
     22 for k, v in z.items():
     23     print(z)
---> 24 model.load_state_dict(z)

~/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py in load_state_dict(self, state_dict, strict)
    837         if len(error_msgs) > 0:
    838             raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
--> 839                                self.__class__.__name__, "\n\t".join(error_msgs)))
    840         return _IncompatibleKeys(missing_keys, unexpected_keys)
    841

RuntimeError: Error(s) in loading state_dict for IODINE:
	While copying the parameter named "x_grid", whose dimensions in the model are torch.Size([1, 1, 1, 28, 28]) and whose dimensions in the checkpoint are torch.Size([1, 1, 1, 28, 28]).
	While copying the parameter named "y_grid", whose dimensions in the model are torch.Size([1, 1, 1, 28, 28]) and whose dimensions in the checkpoint are torch.Size([1, 1, 1, 28, 28]).
	While copying the parameter named "decoder.x_grid", whose dimensions in the model are torch.Size([1, 1, 28, 28]) and whose dimensions in the checkpoint are torch.Size([1, 1, 28, 28]).
	While copying the parameter named "decoder.y_grid", whose dimensions in the model are torch.Size([1, 1, 28, 28]) and whose dimensions in the checkpoint are torch.Size([1, 1, 28, 28]).

All of the sizes are the same.
I am trying to run a version of IODINE i found on github… however, loading the saved dict into my model is trouble.  I do not think I trained it with a parallel GPU, but there is that mode in the program.  It does not match what I believe parallel dict should look like, based on what I’ve seen in other q&as on this site ( because there are keys with less than 7 elements)
Saving the model function looks like this:
	def save(self,save_path,epoch=None):
		print('Saving model at epoch {}'.format(epoch))
		suffix = self.name if epoch is None else self.name+'_epoch_{}.th'.format(epoch)
		model_save_path = save_path + suffix
		torch.save(self.state_dict(),model_save_path)