class_docstr: A pool-based active learner in which a pool holds all available unlabeled data.
  
  It uses a classifier, a query strategy and manages the mutually exclusive partition over the
  whole training data into labeled and unlabeled.
  
  Parameters
  ----------
  clf_factory : small_text.classifiers.factories.AbstractClassifierFactory
      A factory responsible for creating new classifier instances.
  query_strategy : small_text.query_strategies.QueryStrategy
      Query strategy which is responsible for selecting instances during a `query()` call.
  dataset : ~small_text.data.datasets.Dataset
      A training dataset that is supported by the underlying classifier.
  reuse_model : bool, default=False
      Reuses the previous model during retraining (if a previous model exists),
      otherwise creates a new model for each retraining.
  
  Attributes
  ----------
  indices_labeled : numpy.ndarray
      Indices of instances (relative to `self.x_train`) constituting the labeled pool.
  indices_ignored : numpy.ndarray or scipy.sparse.csr_matrix
      Indices of instances (relative to `self.x_train`) which have been ignored,
      i.e. which will never be returned by a query.
  y : numpy.ndarray or scipy.sparse.csr_matrix
      Labels for the the current labeled pool. Each tuple `(x_indices_labeled[i], y[i])`
      represents one labeled sample.
  indices_queried : numpy.ndarray or None
      Queried indices returned by the last `query()` call, or `None` if no query has been
      executed yet.
  fit_kwargs : dict
      Keyword arguments that will be passed to the `fit()` call during `update()`.
docstr: Ignores the sample at the given `x_index.
  
  Any labels which had previously been assigned to this sample will be removed.
  
  Notes
  -----
  If ignoring a sample incurs the removal of a label label, the current model might not
  reflect the labeled data anymore. You should consider if a retraining is necessary when
  using this operation. Since retraining is often time-consuming, `retrain` is set to
  `False` by default.
  
  Parameters
  ----------
  index : int
     Data index (relative to `self.x_train`) for which the label should be ignored.
  retrain : bool, default=False
      Retrains the model after the removal if True.
  indices_validation : numpy.ndarray, default=None
      The given indices (relative to `self.x_indices_labeled`) define a custom validation set
      if provided. This is only used if `retrain` is `True`.
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code: def ignore_sample_at(
      self, index, retrain=False, indices_validation=None
  ):
      """Ignores the sample at the given `x_index.
  
      Any labels which had previously been assigned to this sample will be removed.
  
      Notes
      -----
      If ignoring a sample incurs the removal of a label label, the current model might not
      reflect the labeled data anymore. You should consider if a retraining is necessary when
      using this operation. Since retraining is often time-consuming, `retrain` is set to
      `False` by default.
  
      Parameters
      ----------
      index : int
         Data index (relative to `self.x_train`) for which the label should be ignored.
      retrain : bool, default=False
          Retrains the model after the removal if True.
      indices_validation : numpy.ndarray, default=None
          The given indices (relative to `self.x_indices_labeled`) define a custom validation set
          if provided. This is only used if `retrain` is `True`.
      """
  
      labeling_exists = index in self._index_to_position
      if labeling_exists:
          position = self._index_to_position[index]
          self.y = remove_by_index(self.y, position)
          self.indices_labeled = np.delete(
              self.indices_labeled, position
          )
  
      self.indices_ignored = np.concatenate(
          [self.indices_ignored, [index]]
      )
  
      if labeling_exists and retrain:
          self._retrain(indices_validation=indices_validation)
  
imports: import numpy
  from abc import ABC, abstractmethod
  from pathlib import Path
  from scipy.sparse import csr_matrix
  from small_text.base import LABEL_IGNORED
  from small_text.exceptions import LearnerNotInitializedException
  from small_text.utils.data import list_length
  from small_text.utils.labels import concatenate, get_ignored_labels_mask, remove_by_index
  from small_text.version import __version__
label_desc: Use image node_id 7 for calling the PoolBasedActiveLearner obj's underlying member method code with example usage: obj.ignore_sample_at(index, retrain, indices_validation) without return types