code: def create_train_state(
      params,
      model: nn.Module,
      config: ml_collections.ConfigDict,
      train_steps: int,
  ) -> train_state.TrainState:
      if config.decaying_lr_and_clip_param:
          lr = optax.linear_schedule(
              init_value=config.learning_rate,
              end_value=0.0,
              transition_steps=train_steps,
          )
      else:
          lr = config.learning_rate
      tx = optax.adam(lr)
      state = train_state.TrainState.create(
          apply_fn=model.apply, params=params, tx=tx
      )
      return state
  
imports: import functools
  from typing import Any, Callable
  from absl import logging
  import flax
  from flax import linen
  import agent
  import models
  import test_episodes
  from flax.metrics import tensorboard
  from flax.training import checkpoints
  from flax.training import train_state
  import jax
  import jax.numpy
  import ml_collections
  import numpy
  import optax
label_desc: Use image node_id 7 for calling a global function with example usage: create_train_state(params, model, config, train_steps) and returns: state