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: Sets the batch size of a single forward pass. The batch size 
  can be changed at any time, even after the model was trained.
docstr_tok: ["Sets","the","batch","size","of","a","single","forward","pass",".","The","batch","size","can","be","changed","at","any","time",",","even","after","the","model","was","trained","."]
code: def batch_size(self, value):
      """
      Sets the batch size of a single forward pass. The batch size
      can be changed at any time, even after the model was trained.
      """
      if isinstance(value, int):
          if 0 < value:
              self.estimator.batch_size = value
          else:
              raise ValueError("batch size must be larger than 0")
      else:
          raise TypeError("batch size must be of type integer!")
  
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 7 for calling the DeepActiveLearner obj's underlying member method code with example usage: obj.batch_size(value) without return types