class_docstr: Implementation of locked (or variational) dropout.
  
  Randomly drops out entire parameters in embedding space.
class_docstr_tok: ["Implementation","of","locked","(","or","variational",")","dropout",".","Randomly","drops","out","entire","parameters","in","embedding","space","."]
code: def __init__(
      self, dropout_rate=0.5, batch_first=True, inplace=False
  ) -> None:
      super().__init__()
      self.dropout_rate = dropout_rate
      self.batch_first = batch_first
      self.inplace = inplace
  
file_path: flair/flair/nn/dropout.py
imports: import torch
label_desc: Use image node_id 1 to create a new LockedDropout object from inherited base classes: torch.nn with example: obj = LockedDropout(dropout_rate, batch_first, inplace)