code: def _optimizer(c: Configs):
      """
      #### Set the optimizers for the model
  
      Note that we pass the sharded parameters from `get_trainable_chunk`.
      """
      from labml_nn.optimizers.adam_fp16 import AdamFP16
  
      return AdamFP16(c.model.get_trainable_chunk(), lr=c.learning_rate)
  
file_path: annotated_deep_learning_paper_implementations/labml_nn/scaling/zero3/finetune_neox.py
imports: import datetime
  import torch
  import torch.distributed
  from labml import experiment, monit, tracker
  from labml.configs import option
  from labml.logger import inspect
  from labml_nn.neox.samples.finetune import PipelineParallelTrainerConf
label_desc: Use image node_id 1 for calling a global function with example usage: _optimizer(c) and returns: AdamFP16