code: def DRSensitivity(y, y_pred):
      y_pred = (np.argmax(y_pred, 1) > 0) * 1
      y = (y > 0) * 1
      TP = sum(y_pred * y)
      P = sum(y)
      return float(TP) / P
  
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 3 for calling a global function with example usage: DRSensitivity(y, y_pred) and returns: unknown