code: def get_normalization(
      normalization_type: str,
      eps: Optional[float] = None,
      partial: Optional[float] = None,
  ) -> Tuple[torch.nn.Module, Dict]:
      """Get normalization module and arguments given parameters.
  
      Args:
          normalization_type: Normalization module type.
          eps: Value added to the denominator.
          partial: Value defining the part of the input used for RMS stats (RMSNorm).
  
      Return:
          : Normalization module class
          : Normalization module arguments
  
      """
      norm = {
          "basic_norm": (
              BasicNorm,
              {"eps": eps if eps is not None else 0.25},
          ),
          "layer_norm": (
              torch.nn.LayerNorm,
              {"eps": eps if eps is not None else 1e-12},
          ),
          "rms_norm": (
              RMSNorm,
              {
                  "eps": eps if eps is not None else 1e-05,
                  "partial": partial if partial is not None else -1.0,
              },
          ),
          "scale_norm": (
              ScaleNorm,
              {"eps": eps if eps is not None else 1e-05},
          ),
      }
  
      return norm[normalization_type]
  
file_path: espnet/espnet2/asr_transducer/normalization.py
imports: from typing import Dict, Optional, Tuple
  import torch
label_desc: Use image node_id 1 for calling a global function with example usage: get_normalization(normalization_type, eps, partial) and returns: norm