class_docstr: A class for performing data augmentation using TextAttack.
  
  Returns all possible transformations for a given string. Currently only
      supports transformations which are word swaps.
  
  Args:
      transformation (textattack.Transformation): the transformation
          that suggests new texts from an input.
      constraints: (list(textattack.Constraint)): constraints
          that each transformation must meet
      pct_words_to_swap: (float): [0., 1.], percentage of words to swap per augmented example
      transformations_per_example: (int): Maximum number of augmentations
          per input
      high_yield: Whether to return a set of augmented texts that will be relatively similar, or to return only a
          single one.
      fast_augment: Stops additional transformation runs when number of successful augmentations reaches
          transformations_per_example
      advanced_metrics: return perplexity and USE Score of augmentation
  
  Example::
      >>> from textattack.transformations import WordSwapRandomCharacterDeletion, WordSwapQWERTY, CompositeTransformation
      >>> from textattack.constraints.pre_transformation import RepeatModification, StopwordModification
      >>> from textattack.augmentation import Augmenter
  
      >>> transformation = CompositeTransformation([WordSwapRandomCharacterDeletion(), WordSwapQWERTY()])
      >>> constraints = [RepeatModification(), StopwordModification()]
  
      >>> # initiate augmenter
      >>> augmenter = Augmenter(
      ...     transformation=transformation,
      ...     constraints=constraints,
      ...     pct_words_to_swap=0.5,
      ...     transformations_per_example=3
      ... )
  
      >>> # additional parameters can be modified if not during initiation
      >>> augmenter.enable_advanced_metrics = True
      >>> augmenter.fast_augment = True
      >>> augmenter.high_yield = True
  
      >>> s = 'What I cannot create, I do not understand.'
      >>> results = augmenter.augment(s)
  
      >>> augmentations = results[0]
      >>> perplexity_score = results[1]
      >>> use_score = results[2]
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docstr: Returns all possible augmentations of a list of strings according to
  ``self.transformation``.
  
  Args:
      text_list (list(string)): a list of strings for data augmentation
  Returns a list(string) of augmented texts.
  :param show_progress: show process during augmentation
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code: def augment_many(self, text_list, show_progress=False):
      """Returns all possible augmentations of a list of strings according to
      ``self.transformation``.
  
      Args:
          text_list (list(string)): a list of strings for data augmentation
      Returns a list(string) of augmented texts.
      :param show_progress: show process during augmentation
      """
      if show_progress:
          text_list = tqdm.tqdm(text_list, desc="Augmenting data...")
      return [self.augment(text) for text in text_list]
  
file_path: TextAttack/textattack/augmentation/augmenter.py
imports: import random
  import tqdm
  from textattack.constraints import PreTransformationConstraint
  from textattack.metrics.quality_metrics import Perplexity, USEMetric
  from textattack.shared import AttackedText, utils
label_desc: Use image node_id 4 for calling the Augmenter obj's underlying member method code with example usage: obj.augment_many(text_list, show_progress) and returns: unknown