docstr: Avalilable options relate to BART.
docstr_tok: ["Avalilable","options","relate","to","BART","."]
code: def add_options(cls, parser):
      """Avalilable options relate to BART."""
      group = parser.add_argument_group("Transform/BART")
      group.add(
          "--permute_sent_ratio",
          "-permute_sent_ratio",
          type=float,
          default=0.0,
          help="Permute this proportion of sentences "
          "(boundaries defined by {}) in all inputs.".format(
              DefaultTokens.SENT_FULL_STOPS
          ),
      )
      group.add(
          "--rotate_ratio",
          "-rotate_ratio",
          type=float,
          default=0.0,
          help="Rotate this proportion of inputs.",
      )
      group.add(
          "--insert_ratio",
          "-insert_ratio",
          type=float,
          default=0.0,
          help="Insert this percentage of additional random tokens.",
      )
      group.add(
          "--random_ratio",
          "-random_ratio",
          type=float,
          default=0.0,
          help="Instead of using {}, use random token "
          "this often.".format(DefaultTokens.MASK),
      )
  
      group.add(
          "--mask_ratio",
          "-mask_ratio",
          type=float,
          default=0.0,
          help="Fraction of words/subwords that will be masked.",
      )
      group.add(
          "--mask_length",
          "-mask_length",
          type=str,
          default="subword",
          choices=["subword", "word", "span-poisson"],
          help="Length of masking window to apply.",
      )
      group.add(
          "--poisson_lambda",
          "-poisson_lambda",
          type=float,
          default=3.0,
          help="Lambda for Poisson distribution to sample span length "
          "if `-mask_length` set to span-poisson.",
      )
      group.add(
          "--replace_length",
          "-replace_length",
          type=int,
          default=-1,
          choices=[-1, 0, 1],
          help="When masking N tokens, replace with 0, 1, "
          "or N tokens. (use -1 for N)",
      )
  
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 3 for calling the BARTNoiseTransform obj's underlying member method code with example usage: obj.add_options(cls, parser) without return types