class_docstr: ScaleNorm module definition.
  
  Reference: https://arxiv.org/pdf/1910.05895.pdf
  
  Args:
      normalized_shape: Expected size.
      eps: Value added to the denominator for numerical stability.
class_docstr_tok: ["ScaleNorm","module","definition",".","Reference",":","https",":","\/\/arxiv.org\/pdf\/1910.05895.pdf","Args",":","normalized_shape",":","Expected","size",".","eps",":","Value","added","to","the","denominator","for","numerical","stability","."]
docstr: Construct a ScaleNorm object.
docstr_tok: ["Construct","a","ScaleNorm","object","."]
code: def __init__(self, normalized_shape: int, eps: float = 1e-5) -> None:
      """Construct a ScaleNorm object."""
      super().__init__()
  
      self.eps = eps
      self.scale = torch.nn.Parameter(
          torch.tensor(normalized_shape**0.5)
      )
  
file_path: espnet/espnet2/asr_transducer/normalization.py
imports: from typing import Dict, Optional, Tuple
  import torch
label_desc: Use image node_id 1 to create a new ScaleNorm object from inherited base classes: torch.nn with example: obj = ScaleNorm(normalized_shape, eps)