class_docstr: Abstract generator class.
      
docstr: Compute inputs for the network using an image_group.
          
code: def compute_inputs(self, image_group):
      """Compute inputs for the network using an image_group."""
      # get the max image shape
      max_shape = tuple(
          max(image.shape[x] for image in image_group) for x in range(3)
      )
  
      # construct an image batch object
      image_batch = np.zeros(
          (self.batch_size,) + max_shape, dtype=keras.backend.floatx()
      )
  
      # copy all images to the upper left part of the image batch object
      for image_index, image in enumerate(image_group):
          image_batch[
              image_index,
              : image.shape[0],
              : image.shape[1],
              : image.shape[2],
          ] = image
  
      if keras.backend.image_data_format() == "channels_first":
          image_batch = image_batch.transpose((0, 3, 1, 2))
  
      return image_batch
  
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 24 for calling the Generator obj's underlying member method code with example usage: obj.compute_inputs(image_group) and returns: image_batch