class_docstr: ```
  Main class used to tune and train Keras models
  using Array data.  An objects of this class should be instantiated
  via the ktrain.get_learner method instead of directly.
  Main parameters are:
  
  
  model (Model):        A compiled instance of keras.engine.training.Model
  train_data (ndarray): A tuple of (x_train, y_train), where x_train and
                        y_train are numpy.ndarrays.
  val_data (ndarray):   A tuple of (x_test, y_test), where x_test and
                        y_test are numpy.ndarrays.
  ```
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code: def __init__(
      self,
      model,
      train_data=None,
      val_data=None,
      batch_size=U.DEFAULT_BS,
      eval_batch_size=U.DEFAULT_BS,
      workers=1,
      use_multiprocessing=False,
  ):
      super().__init__(
          model,
          workers=workers,
          use_multiprocessing=use_multiprocessing,
      )
      self.train_data = train_data
      self.val_data = val_data
      self.batch_size = batch_size
      self.eval_batch_size = eval_batch_size
      return
  
imports: from .None import utils
  from .graph.predictor import LinkPredictor, NodePredictor
  from .graph.preprocessor import LinkPreprocessor, NodePreprocessor
  from .imports import *
  from .lroptimize.lrfinder import *
  from .tabular.predictor import TabularPredictor
  from .tabular.preprocessor import TabularPreprocessor
  from .text.ner.predictor import NERPredictor
  from .text.ner.preprocessor import NERPreprocessor
  from .text.predictor import TextPredictor
  from .text.preprocessor import BERTPreprocessor, TextPreprocessor, TransformersPreprocessor
  from .vision.predictor import ImagePredictor
  from .vision.preprocessor import ImagePreprocessor
label_desc: Use image node_id 1 to create a new ArrayLearner object from inherited base classes: Learner with example: obj = ArrayLearner(model, train_data, val_data, batch_size, eval_batch_size, workers, use_multiprocessing)