parameters: (self, input_data)
docstring: Trains the model with multiple sets of images for multiple classes.
  
      This essentially calls :func:`train` for each class of images you provide. You can use this to
      add a batch of new classes or retrain existing classes. Just beware that if you've already added
      new classes using the imprinting API, then the data input here must include the same classes in
      the same order. Alternatively, you can use :func:`train` to retrain specific classes one at a
      time.
  
      Args:
        input_data (list of :obj:`numpy.array`): The images to train for multiple classes.
          Each :obj:`numpy.array` in the list represents a different class, which
          itself contains a list of :obj:`numpy.array` objects, which each represent an image as a 1-D
          tensor. You can convert each image to this format by passing it as a :class:`PIL.Image` to
          :func:`numpy.asarray()`.
docstring_summary: Trains the model with multiple sets of images for multiple classes.
docstring_tokens: Trains the model with multiple sets of images for multiple classes .
function: def train_all(self, input_data):
      """Trains the model with multiple sets of images for multiple classes.
  
      This essentially calls :func:`train` for each class of images you provide. You can use this to
      add a batch of new classes or retrain existing classes. Just beware that if you've already added
      new classes using the imprinting API, then the data input here must include the same classes in
      the same order. Alternatively, you can use :func:`train` to retrain specific classes one at a
      time.
  
      Args:
        input_data (list of :obj:`numpy.array`): The images to train for multiple classes.
          Each :obj:`numpy.array` in the list represents a different class, which
          itself contains a list of :obj:`numpy.array` objects, which each represent an image as a 1-D
          tensor. You can convert each image to this format by passing it as a :class:`PIL.Image` to
          :func:`numpy.asarray()`.
      """
      for class_id, tensors in enumerate(input_data):
        self.train(tensors, class_id=self._num_classes + class_id)
function_tokens: def train_all ( self , input_data ) : for class_id , tensors in enumerate ( input_data ) : self . train ( tensors , class_id = self . _num_classes + class_id )