class_docstr: This class is an model of a general deep active learning algorithm.
  Differences to the classical ActiveLearner are:
      - Data is no member variable of the DeepActiveLearner class
      - Misses the initial add/train data methods, therefore always trains on new data
      - Uses different interfaces to sklearn in some functions
  
  Args:
      estimator: The estimator to be used in the active learning loop.
      query_strategy: Function providing the query strategy for the active learning loop,
          for instance, modAL.uncertainty.uncertainty_sampling.
      on_transformed: Whether to transform samples with the pipeline defined by the estimator
          when applying the query strategy.
      **fit_kwargs: keyword arguments.
  
  Attributes:
      estimator: The estimator to be used in the active learning loop.
      query_strategy: Function providing the query strategy for the active learning loop.
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docstr: Interface for the fit method of the predictor. Fits the predictor to the supplied data.
  
  Args:
      X: The samples to be fitted.
      y: The corresponding labels.
      bootstrap: If true, trains the estimator on a set bootstrapped from X.
          Useful for building Committee models with bagging.
      **fit_kwargs: Keyword arguments to be passed to the fit method of the predictor.
  
  Returns:
      self
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code: def fit(
      self,
      X: modALinput,
      y: modALinput,
      bootstrap: bool = False,
      **fit_kwargs,
  ) -> "BaseLearner":
      """
      Interface for the fit method of the predictor. Fits the predictor to the supplied data.
  
      Args:
          X: The samples to be fitted.
          y: The corresponding labels.
          bootstrap: If true, trains the estimator on a set bootstrapped from X.
              Useful for building Committee models with bagging.
          **fit_kwargs: Keyword arguments to be passed to the fit method of the predictor.
  
      Returns:
          self
      """
      return self._fit_on_new(X, y, bootstrap=bootstrap, **fit_kwargs)
  
imports: from typing import Any, Callable, List, Optional, Tuple
  import numpy
  from modAL.acquisition import max_EI
  from modAL.disagreement import max_std_sampling, vote_entropy_sampling
  from modAL.models.base import BaseCommittee, BaseLearner
  from modAL.uncertainty import uncertainty_sampling
  from modAL.utils.data import data_vstack, modALinput, retrieve_rows
  from modAL.utils.validation import check_class_labels, check_class_proba
  from sklearn.base import BaseEstimator
  from sklearn.metrics import accuracy_score
  from sklearn.utils import check_X_y
label_desc: Use image node_id 2 for calling the DeepActiveLearner obj's underlying member method code with example usage: obj.fit(X, y, bootstrap) and returns: self