docstr: Parameters
  ----------
  n_tasks: int
    Number of tasks
  image_size: int
    Resolution of the input images(square)
  n_downsample: int
    Downsample ratio in power of 2
  n_init_kernel: int
    Kernel size for the first convolutional layer
  n_fully_connected: list of int
    Shape of FC layers after convolutions
  n_classes: int
    Number of classes to predict (only used in classification mode)
  augment: bool
    If to use data augmentation
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code: def __init__(
      self,
      n_tasks=1,
      image_size=512,
      n_downsample=6,
      n_init_kernel=16,
      n_fully_connected=[1024],
      n_classes=5,
      augment=False,
      **kwargs,
  ):
      """
      Parameters
      ----------
      n_tasks: int
        Number of tasks
      image_size: int
        Resolution of the input images(square)
      n_downsample: int
        Downsample ratio in power of 2
      n_init_kernel: int
        Kernel size for the first convolutional layer
      n_fully_connected: list of int
        Shape of FC layers after convolutions
      n_classes: int
        Number of classes to predict (only used in classification mode)
      augment: bool
        If to use data augmentation
      """
      self.n_tasks = n_tasks
      self.image_size = image_size
      self.n_downsample = n_downsample
      self.n_init_kernel = n_init_kernel
      self.n_fully_connected = n_fully_connected
      self.n_classes = n_classes
      self.augment = augment
      super(DRModel, self).__init__(**kwargs)
      self.build_graph()
  
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 DRModel object from inherited base classes: TensorGraph with example: obj = DRModel(n_tasks, image_size, n_downsample, n_init_kernel, n_fully_connected, n_classes, augment)