code: def __init__(
      self, output_height, output_width, batch_size, resize_only
  ):
      tf = _lazy_import_tensorflow()
      self.batch_size = batch_size
      self.graph = tf.Graph()
      self.resize_only = resize_only
      with self.graph.as_default():
          self.img_tf = [
              tf.placeholder(tf.float32, [None, None, 3])
              for x in range(0, self.batch_size)
          ]
          self.ann_tf = [
              tf.placeholder(tf.float32, [None, 6])
              for x in range(0, self.batch_size)
          ]
          self.alpha_tf = tf.placeholder(
              tf.float32, [self.batch_size, 4]
          )
          self.random_seed_tf = tf.placeholder(tf.uint32)
          self.resize_op_batch = []
          for i in range(0, self.batch_size):
              if resize_only:
                  aug_img_tf, aug_ann_tf = resize_augmenter(
                      self.img_tf[i],
                      self.ann_tf[i],
                      (output_height, output_width),
                  )
                  self.resize_op_batch.append([aug_img_tf, aug_ann_tf])
              else:
                  aug_img_tf, aug_ann_tf = complete_augmenter(
                      self.img_tf[i],
                      self.ann_tf[i],
                      self.random_seed_tf[i],
                      self.alpha_tf[i],
                      output_height,
                      output_width,
                  )
                  self.resize_op_batch.append([aug_img_tf, aug_ann_tf])
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import numpy
  from PIL import Image
  import turicreate.toolkits._tf_utils
  import turicreate
  from turicreate._deps.minimal_package import _minimal_package_import_check
label_desc: Use image node_id 1 to create a new DataAugmenter object from inherited base classes: object with example: obj = DataAugmenter(output_height, output_width, batch_size, resize_only)