class_docstr: Noise from BART.
class_docstr_tok: ["Noise","from","BART","."]
code: def __repr__(self):
      cls_name = type(self).__name__
      kwargs = {}
      if self.permute_sent_ratio > 0.0:
          kwargs["permute_sent_ratio"] = self.permute_sent_ratio
          kwargs["full_stop_token"] = self.full_stop_token
      if self.insert_ratio > 0.0:
          kwargs["insert_ratio"] = self.insert_ratio
      if self.rotate_ratio > 0.0:
          kwargs["rotate_ratio"] = self.rotate_ratio
      if self.random_ratio > 0.0:
          kwargs["random_ratio"] = self.random_ratio
      if self.mask_ratio > 0.0:
          kwargs["mask_ratio"] = self.mask_ratio
          kwargs["mask_length"] = self.mask_length
          kwargs["poisson_lambda"] = self.poisson_lambda
          kwargs["replace_length"] = self.replace_length
      cls_args = ", ".join(
          [f"{kw}={arg}" for kw, arg in kwargs.items()]
      )
      return "{}({})".format(cls_name, cls_args)
  
imports: import math
  import numpy
  import torch
  from typing import Sequence, Callable
  from onmt.constants import DefaultTokens, SubwordMarker
  from onmt.transforms import register_transform
  from .transform import Transform
label_desc: Use image node_id 10 for calling the BARTNoising obj's underlying member method code with example usage: obj.__repr__() and returns: str