code: def apply_gradients(self, grads: State, **kwargs) -> "TrainState[M]":
      updates, opt_state = self.tx.update(
          grads, self.opt_state, self.params
      )
      params = optax.apply_updates(self.params, updates)  # type: ignore
      step = self.step + 1
      return self.replace(
          params=params,
          opt_state=opt_state,
          step=step,
          **kwargs,
      )
  
imports: from __future__ import annotations
  import inspect
  import typing
  import jax
  import jax.numpy
  import numpy
  import optax
  from flax.experimental.nnx.nnx import pytreelib
  from flax.experimental.nnx.nnx.module import GraphDef, Module
  from flax.experimental.nnx.nnx.proxy_caller import ApplyCaller
  from flax.experimental.nnx.nnx.rnglib import Rngs
  from flax.experimental.nnx.nnx.state import State
label_desc: Use image node_id 3 for calling the TrainState obj's underlying member method code with example usage: obj.apply_gradients(grads) and returns: self