code: def from_arrays(cls, path, trn, val, bs=64, tfms=(None,None), classes=None, num_workers=4, test=None, continuous=False):
          
          f = ArraysIndexRegressionDataset if continuous else ArraysIndexDataset
          datasets = cls.get_ds(f, trn, val, tfms, test=test)
          return cls(path, datasets, bs, num_workers, classes=classes)
docs: Read in images and their labels given as numpy arrays
  
          Arguments:
              path: a root path of the data (used for storing trained models, precomputed values, etc)
              trn: a tuple of training data matrix and target label/classification array (e.g. `trn=(x,y)` where `x` has the
                  shape of `(5000, 784)` and `y` has the shape of `(5000,)`)
              val: a tuple of validation data matrix and target label/classification array.
              bs: batch size
              tfms: transformations (for data augmentations). e.g. output of `tfms_from_model`
              classes: a list of all labels/classifications
              num_workers: a number of workers
              test: a matrix of test data (the shape should match `trn[0]`)
  
          Returns:
              ImageClassifierData