You could use the state_dict of the pretrained resnet, manipulate its keys to match the layer names of your new model (assuming all parameters have the same shape), and load it to your model.
Something like this should work:
model = NutSnackClassication()
reference = models.resnet34(pretrained=True)
sd = reference.state_dict()

# change the keys of sd here

model.load_state_dict(sd)