class_docstr: The `HfTrainerDeepSpeedConfig` object is meant to be created during `TrainingArguments` object creation and has the
  same lifespan as the latter.
docstr: A utility method that massages the config file and can optionally verify that the values match.
  
  1. Replace "auto" values with `TrainingArguments` value.
  
  2. If it wasn't "auto" and `must_match` is true, then check that DS config matches Trainer
  config values and if mismatched add the entry to `self.mismatched` - will assert during
  `trainer_config_finalize` for one or more mismatches.
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code: def fill_match(
      self, ds_key_long, hf_val, hf_key=None, must_match=True
  ):
      """
      A utility method that massages the config file and can optionally verify that the values match.
  
      1. Replace "auto" values with `TrainingArguments` value.
  
      2. If it wasn't "auto" and `must_match` is true, then check that DS config matches Trainer
      config values and if mismatched add the entry to `self.mismatched` - will assert during
      `trainer_config_finalize` for one or more mismatches.
  
      """
      config, ds_key = self.find_config_node(ds_key_long)
      if config is None:
          return
  
      if config.get(ds_key) == "auto":
          config[ds_key] = hf_val
          return
  
      if not must_match:
          return
  
      ds_val = config.get(ds_key)
      if ds_val is not None and ds_val != hf_val:
          self.mismatches.append(
              f"- ds {ds_key_long}={ds_val} vs hf {hf_key}={hf_val}"
          )
  
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 4 for calling the HfTrainerDeepSpeedConfig obj's underlying member method code with example usage: obj.fill_match(ds_key_long, hf_val, hf_key, must_match) without return types