code: def update_adapter_cfg_input_dim(
      module: torch.nn.Module, cfg: DictConfig, *, module_dim: int
  ):
      """
      Update the input dimension of the provided adapter config with some default value.
  
      Args:
          module: The module that implements AdapterModuleMixin.
          cfg: A DictConfig or a Dataclass representing the adapter config.
          module_dim: A default module dimension, used if cfg has an incorrect input dimension.
  
      Returns:
          A DictConfig representing the adapter's config.
      """
      cfg = convert_adapter_cfg_to_dict_config(cfg)
  
      input_dim_valid_keys = ["in_features", "n_feat"]
      input_key = None
  
      for key in input_dim_valid_keys:
          if key in cfg:
              input_key = key
              break
  
      if input_key is None:
          raise ValueError(
              f"Failed to infer the input dimension of the Adapter cfg. \nExpected one of : {input_dim_valid_keys}.\n"
              f"Provided config : \n"
              f"{OmegaConf.to_yaml(cfg)}"
          )
  
      input_dim = cfg[input_key]
  
      if input_dim != module_dim:
          logging.info(
              f"Updating {module.__class__.__name__} Adapter input dim from {input_dim} to {module_dim}"
          )
          input_dim = module_dim
  
      cfg[input_key] = input_dim
      return cfg
  
imports: from dataclasses import is_dataclass
  import torch
  from omegaconf import DictConfig, OmegaConf
  from nemo.utils import logging
label_desc: Use image node_id 2 for calling a global function with example usage: update_adapter_cfg_input_dim(module, cfg) and returns: cfg