class_docstr: Adaptive variant of `UniformRegularizer` which learns the mix-in noise distribution.
  
  Learning Better Structured Representations using Low-Rank Adaptive Label Smoothing
  (Ghoshal+ 2021; https://openreview.net/pdf?id=5NsEIflpbSv)
class_docstr_tok: ["Adaptive","variant","of","`","UniformRegularizer","`","which","learns","the","mix-in","noise","distribution",".","Learning","Better","Structured","Representations","using","Low-Rank","Adaptive","Label","Smoothing","(","Ghoshal+","2021",";","https",":","\/\/openreview.net\/pdf","?","id=5NsEIflpbSv",")"]
code: def __init__(self, config, ignore_index=1):
      super().__init__(config, ignore_index)
  
      if config.eta < 0:
          raise ValueError("eta must be >= 0")
      if (
          config.label_embedding_dropout < 0
          or config.label_embedding_dropout >= 1
      ):
          raise ValueError("label_embedding_dropout must be [0, 1)")
  
      self.eta = config.eta
      self.label_embedding_dim = config.label_embedding_dim
      self.label_embedding_dropout = config.label_embedding_dropout
      self.label_embedding = None
  
file_path: pytext/pytext/loss/regularizer.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from pytext.config import ConfigBase
  from .loss import Loss
label_desc: Use image node_id 1 to create a new AdaptiveRegularizer object from inherited base classes: Regularizer with example: obj = AdaptiveRegularizer(config, ignore_index)