class_docstr: The `HfTrainerDeepSpeedConfig` object is meant to be created during `TrainingArguments` object creation and has the
  same lifespan as the latter.
docstr: This stage is run after we have the model and know num_training_steps.
  
  Now we can complete the configuration process.
docstr_tok: ["This","stage","is","run","after","we","have","the","model","and","know","num_training_steps",".","Now","we","can","complete","the","configuration","process","."]
code: def trainer_config_finalize(self, args, model, num_training_steps):
      """
      This stage is run after we have the model and know num_training_steps.
  
      Now we can complete the configuration process.
      """
      # zero
  
      # deal with config keys that use `auto` value and rely on model's hidden_size
      hidden_size_based_keys = [
          "zero_optimization.reduce_bucket_size",
          "zero_optimization.stage3_prefetch_bucket_size",
          "zero_optimization.stage3_param_persistence_threshold",
      ]
      hidden_size_auto_keys = [
          x for x in hidden_size_based_keys if self.is_auto(x)
      ]
  
      if len(hidden_size_auto_keys) > 0:
          if hasattr(model.config, "hidden_size"):
              hidden_size = model.config.hidden_size
          elif hasattr(model.config, "hidden_sizes"):
              # if there are many hidden sizes pick the largest one
              hidden_size = max(model.config.hidden_sizes)
          else:
              raise ValueError(
                  "The model's config file has neither `hidden_size` nor `hidden_sizes` entry, "
                  "therefore it's not possible to automatically fill out the following `auto` entries "
                  f"in the DeepSpeed config file: {hidden_size_auto_keys}. You can fix that by replacing "
                  "`auto` values for these keys with an integer value of your choice."
              )
  
          self.fill_only(
              "zero_optimization.reduce_bucket_size",
              hidden_size * hidden_size,
          )
          if self.is_zero3():
              # automatically assign the optimal config values based on model config
              self.fill_only(
                  "zero_optimization.stage3_prefetch_bucket_size",
                  0.9 * hidden_size * hidden_size,
              )
              self.fill_only(
                  "zero_optimization.stage3_param_persistence_threshold",
                  10 * hidden_size,
              )
  
      # scheduler
      self.fill_match(
          "scheduler.params.total_num_steps",
          num_training_steps,
          "num_training_steps (calculated)",
      )
      self.fill_match(
          "scheduler.params.warmup_num_steps",
          args.get_warmup_steps(num_training_steps),
          "warmup_steps",
      )
  
      if len(self.mismatches) > 0:
          mismatches = "\n".join(self.mismatches)
          raise ValueError(
              "Please correct the following DeepSpeed config values that mismatch TrainingArguments"
              f" values:\n{mismatches}\nThe easiest method is to set these DeepSpeed config values to 'auto'."
          )
  
imports: import importlib.metadata
  import importlib.util
  import weakref
  from functools import partialmethod
  from ..dependency_versions_check import dep_version_check
  from ..utils import is_accelerate_available, is_torch_available, logging
label_desc: Use image node_id 6 for calling the HfTrainerDeepSpeedConfig obj's underlying member method code with example usage: obj.trainer_config_finalize(args, model, num_training_steps) without return types