code: def create_tensor(self, in_layers=None, set_tensors=True, **kwargs):
      inputs = self._get_input_tensors(in_layers)
      parent_tensor = inputs[0]
      training = kwargs["training"] if "training" in kwargs else 1.0
  
      parent_tensor = parent_tensor / 255.0
      if not self.augment:
          out_tensor = parent_tensor
      else:
  
          def preprocess(img):
              img = tf.image.random_flip_left_right(img)
              img = tf.image.random_flip_up_down(img)
              img = tf.image.rot90(img, k=np.random.randint(0, 4))
              if self.distort_color:
                  img = tf.image.random_brightness(
                      img, max_delta=32.0 / 255.0
                  )
                  img = tf.image.random_saturation(
                      img, lower=0.5, upper=1.5
                  )
                  img = tf.clip_by_value(img, 0.0, 1.0)
              if self.central_crop:
                  # sample cut ratio from a clipped gaussian
                  img = tf.image.central_crop(
                      img,
                      np.clip(np.random.normal(1.0, 0.06), 0.8, 1.0),
                  )
                  img = tf.image.resize_bilinear(
                      tf.expand_dims(img, 0),
                      tf.convert_to_tensor(self.size),
                  )[0]
              return img
  
          outs = tf.map_fn(preprocess, parent_tensor)
          # train/valid differences
          out_tensor = training * outs + (1 - training) * parent_tensor
      if set_tensors:
          self.out_tensor = out_tensor
      return out_tensor
  
imports: import numpy
  import tensorflow
  from deepchem.data import NumpyDataset, pad_features
  from deepchem.metrics import to_one_hot
  from deepchem.models.tensorgraph.layers import Layer, Dense, SoftMax, Reshape, SparseSoftMaxCrossEntropy, BatchNorm, Conv2D, MaxPool2D, WeightedError, Dropout, ReLU, Stack, Flatten, ReduceMax, WeightDecay
  from deepchem.models.tensorgraph.layers import L2Loss, Label, Weights, Feature
  from deepchem.models.tensorgraph.tensor_graph import TensorGraph
  from deepchem.trans import undo_transforms
  from deepchem.data.data_loader import ImageLoader
  from sklearn.metrics import confusion_matrix, accuracy_score
label_desc: Use image node_id 2 for calling the DRAugment obj's underlying member method code with example usage: obj.create_tensor(in_layers, set_tensors) and returns: out_tensor, img