class_docstr: Returns flattened versions of images read in from disk. This dataset can be
  used with the default neighbour retrieval method used by ivis (Annoy KNN index)
  since it is 2D.
class_docstr_tok: ["Returns","flattened","versions","of","images","read","in","from","disk",".","This","dataset","can","be","used","with","the","default","neighbour","retrieval","method","used","by","ivis","(","Annoy","KNN","index",")","since","it","is","2D","."]
code: def __init__(
      self,
      filepath_list,
      img_shape,
      color_mode="rgb",
      resize_method="bilinear",
      preserve_aspect_ratio=False,
      dtype=tf.uint8,
      preprocessing_function=None,
      n_jobs=None,
  ):
      super().__init__(
          filepath_list,
          img_shape,
          color_mode,
          resize_method,
          preserve_aspect_ratio,
          dtype,
          preprocessing_function,
          n_jobs=n_jobs,
      )
      self.shape = (self.shape[0], np.prod(self.shape[1:]))
  
file_path: ivis/ivis/data/sequence/image.py
imports: import os
  from concurrent.futures import ThreadPoolExecutor
  import numpy
  import tensorflow
  from .sequence import IndexableDataset
label_desc: Use image node_id 1 to create a new FlattenedImageDataset object from inherited base classes: ImageDataset with example: obj = FlattenedImageDataset(filepath_list, img_shape, color_mode, resize_method, preserve_aspect_ratio, dtype, preprocessing_function, n_jobs)