class_docstr: Abstract generator class.
      
docstr: Compute inputs and target outputs for the network.
          
code: def compute_input_output(self, group):
      """Compute inputs and target outputs for the network."""
      # load images and annotations
      image_group = self.load_image_group(group)
      annotations_group = self.load_annotations_group(group)
  
      # check validity of annotations
      image_group, annotations_group = self.filter_annotations(
          image_group, annotations_group, group
      )
  
      # randomly apply visual effect
      image_group, annotations_group = self.random_visual_effect_group(
          image_group, annotations_group
      )
  
      # randomly transform data
      image_group, annotations_group = self.random_transform_group(
          image_group, annotations_group
      )
  
      # perform preprocessing steps
      image_group, annotations_group = self.preprocess_group(
          image_group, annotations_group
      )
  
      # compute network inputs
      inputs = self.compute_inputs(image_group)
  
      # compute network targets
      targets = self.compute_targets(image_group, annotations_group)
  
      return inputs, targets
  
imports: import numpy
  import random
  import warnings
  from tensorflow import keras
  from ..utils.anchors import anchor_targets_bbox, anchors_for_shape, guess_shapes
  from ..utils.config import parse_anchor_parameters, parse_pyramid_levels
  from ..utils.image import TransformParameters, adjust_transform_for_image, apply_transform, preprocess_image, resize_image
  from ..utils.transform import transform_aabb
label_desc: Use image node_id 27 for calling the Generator obj's underlying member method code with example usage: obj.compute_input_output(group) and returns: inputs,  targets