parames_to_update=model_ft.parameters()

This line of code retrieves all the parameters of a pre-trained model called `model_ft` that need to be updated during fine-tuning.

During fine-tuning, we want to update only a subset of the parameters in the pre-trained model. This is because the lower layers of the pre-trained model have already learned useful features that we want to retain, so we don't want to update those parameters. On the other hand, the higher layers of the pre-trained model are more specific to the original task for which it was trained, so we want to update those parameters to better fit our new task.

By calling `model_ft.parameters()`, we obtain an iterator over all the parameters in the model. We can then select the parameters that need to be updated by filtering out the ones we want to keep fixed. This is typically done by setting the `requires_grad` attribute of each parameter to `False` or `True` depending on whether we want to freeze it or update it during fine-tuning.