class_docstr: BasicNorm module definition.
  
  Reference: https://github.com/k2-fsa/icefall/pull/288
  
  Args:
      normalized_shape: Expected size.
      eps: Value added to the denominator for numerical stability.
class_docstr_tok: ["BasicNorm","module","definition",".","Reference",":","https",":","\/\/github.com\/k2-fsa\/icefall\/pull\/288","Args",":","normalized_shape",":","Expected","size",".","eps",":","Value","added","to","the","denominator","for","numerical","stability","."]
docstr: Construct a BasicNorm object.
docstr_tok: ["Construct","a","BasicNorm","object","."]
code: def __init__(
      self,
      normalized_shape: int,
      eps: float = 0.25,
  ) -> None:
      """Construct a BasicNorm object."""
      super().__init__()
  
      self.eps = torch.nn.Parameter(torch.tensor(eps).log().detach())
  
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 BasicNorm object from inherited base classes: torch.nn with example: obj = BasicNorm(normalized_shape, eps)