class_docstr: RMSNorm module definition.
  
  Reference: https://arxiv.org/pdf/1910.07467.pdf
  
  Args:
      normalized_shape: Expected size.
      eps: Value added to the denominator for numerical stability.
      partial: Value defining the part of the input used for RMS stats.
class_docstr_tok: ["RMSNorm","module","definition",".","Reference",":","https",":","\/\/arxiv.org\/pdf\/1910.07467.pdf","Args",":","normalized_shape",":","Expected","size",".","eps",":","Value","added","to","the","denominator","for","numerical","stability",".","partial",":","Value","defining","the","part","of","the","input","used","for","RMS","stats","."]
docstr: Construct a RMSNorm object.
docstr_tok: ["Construct","a","RMSNorm","object","."]
code: def __init__(
      self,
      normalized_shape: int,
      eps: float = 1e-5,
      partial: float = 0.0,
  ) -> None:
      """Construct a RMSNorm object."""
      super().__init__()
  
      self.normalized_shape = normalized_shape
  
      self.partial = True if 0 < partial < 1 else False
      self.p = partial
      self.eps = eps
  
      self.scale = torch.nn.Parameter(torch.ones(normalized_shape))
  
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 RMSNorm object from inherited base classes: torch.nn with example: obj = RMSNorm(normalized_shape, eps, partial)