docstr: Parameters
  ----------
  augment: bool
    If to use data augmentation
  batch_size: int
    Number of images in the batch
  distort_color: bool
    If to apply random distortion on the color
  central_crop: bool
    If to randomly crop the sample around the center
  size: int
    Resolution of the input images(square)
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code: def __init__(
      self,
      augment,
      batch_size,
      distort_color=True,
      central_crop=True,
      size=(512, 512),
      **kwargs,
  ):
      """
      Parameters
      ----------
      augment: bool
        If to use data augmentation
      batch_size: int
        Number of images in the batch
      distort_color: bool
        If to apply random distortion on the color
      central_crop: bool
        If to randomly crop the sample around the center
      size: int
        Resolution of the input images(square)
      """
      self.augment = augment
      self.batch_size = batch_size
      self.distort_color = distort_color
      self.central_crop = central_crop
      self.size = size
      super(DRAugment, self).__init__(**kwargs)
  
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 1 to create a new DRAugment object from inherited base classes: Layer with example: obj = DRAugment(augment, batch_size, distort_color, central_crop, size)