Update docs/en/tutorials/optim_wrapper.md

docs/en/tutorials/optim_wrapper.md

@@ -211,7 +211,7 @@ optim_wrapper_new.load_state_dict(optim_state_dict)
 
 Considering that algorithms like GANs usually need to use multiple optimizers to train the generator and the discriminator, MMEngine provides a container class called `OptimWrapperDict` to manage them. `OptimWrapperDict` stores the sub-OptimWrapper in the form of `dict`, and can be accessed and traversed just like a `dict`.
 
-Unlike regular OptimWrapper, `OptimWrapperDict` does not provide methods such as `update_prarms`, `optim_context`, `backward`, `step`, etc. therefore, it cannot be used directly to train models. We suggest implementing the logic of parameter updating by accessing the sub-OptimWarpper in `OptimWrapperDict` directly.
+Unlike regular OptimWrapper, `OptimWrapperDict` does not provide methods such as `update_prarms`, `optim_context`, `backward`, `step`, etc. Therefore, it cannot be used directly to train models. We suggest implementing the logic of parameter updating by accessing the sub-OptimWarpper in `OptimWrapperDict` directly.
 
 Users may wonder why not just use `dict` to manage multiple optimizers since `OptimWrapperDict` does not have training capabilities. Actually, the core function of `OptimWrapperDict` is to support exporting or loading the state dictionary of all sub-OptimWrapper and to support getting learning rates and momentums as well. Without `OptimWrapperDict`, MMEngine needs to do a lot of `if-else` in OptimWrapper to get the states of the `OptimWrappers`.