class_docstr: Object that perform the active learning iteration.
  
  Args:
      dataset (ActiveLearningDataset): Dataset with some sample already labelled.
      get_probabilities (Function): Dataset -> **kwargs ->
                                      ndarray [n_samples, n_outputs, n_iterations].
      heuristic (Heuristic): Heuristic from baal.active.heuristics.
      query_size (int): Number of sample to label per step.
      max_sample (int): Limit the number of sample used (-1 is no limit).
      uncertainty_folder (Optional[str]): If provided, will store uncertainties on disk.
      ndata_to_label (int): DEPRECATED, please use `query_size`.
      **kwargs: Parameters forwarded to `get_probabilities`.
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code: def __init__(
      self,
      dataset: ActiveLearningDataset,
      get_probabilities: Callable,
      heuristic: heuristics.AbstractHeuristic = heuristics.Random(),
      query_size: int = 1,
      max_sample=-1,
      uncertainty_folder=None,
      ndata_to_label=None,
      **kwargs,
  ) -> None:
      if ndata_to_label is not None:
          warnings.warn(
              "`ndata_to_label` is deprecated, please use `query_size`.",
              DeprecationWarning,
          )
          query_size = ndata_to_label
      self.query_size = query_size
      self.get_probabilities = get_probabilities
      self.heuristic = heuristic
      self.dataset = dataset
      self.max_sample = max_sample
      self.uncertainty_folder = uncertainty_folder
      self.kwargs = kwargs
  
file_path: baal/baal/active/active_loop.py
imports: import os
  import pickle
  import types
  import warnings
  from typing import Callable
  import numpy
  import structlog
  import torch.utils.data
  from .None import heuristics
  from .dataset import ActiveLearningDataset
label_desc: Use image node_id 1 to create a new ActiveLearningLoop object  with example: obj = ActiveLearningLoop(dataset, get_probabilities, heuristic, query_size, max_sample, uncertainty_folder, ndata_to_label)