code: def __init__(
      self, multi_label=False, device=None, mini_batch_size=32
  ):
      self.multi_label = multi_label
      self.mini_batch_size = mini_batch_size
  
      if device is None:
          self.device = "cuda" if torch.cuda.is_available() else "cpu"
      else:
          self.device = device
  
      if self.device.startswith("cuda"):
          logging.info("torch.version.cuda: %s", torch.version.cuda)
          logging.info(
              "torch.cuda.is_available(): %s", torch.cuda.is_available()
          )
          if torch.cuda.is_available():
              logging.info(
                  "torch.cuda.current_device(): %s",
                  torch.cuda.current_device(),
              )
  
imports: import logging
  import warnings
  from abc import abstractmethod
  from functools import partial
  from pathlib import Path
  from small_text.classifiers.classification import Classifier
  from small_text.integrations.pytorch.exceptions import PytorchNotFoundError
  from small_text.training.early_stopping import EarlyStopping, NoopEarlyStopping, EarlyStoppingOrCondition
  from small_text.training.metrics import Metric
  from small_text.training.model_selection import ModelSelection, NoopModelSelection
  from small_text.utils.classification import empty_result, prediction_result
label_desc: Use image node_id 1 to create a new PytorchClassifier object from inherited base classes: PytorchModelSelectionMixin, Classifier with example: obj = PytorchClassifier(multi_label, device, mini_batch_size)