class_docstr: A Binary AUC metric for binary classification tasks.
  
  For update state, the shapes of y_true and y_pred are expected to be:
    - y_true: [batch_size x 1] array of 0 for negatives and 1 for positives
    - y_pred: [batch_size x 2] array of probabilities where y_pred[:,0] are the
      probabilities of the 0th(negative) class and y_pred[:,1] are the
      probabilities of the 1st(positive) class
  
  See https://www.tensorflow.org/api_docs/python/tf/keras/metrics/AUC for
    details.
class_docstr_tok: ["A","Binary","AUC","metric","for","binary","classification","tasks",".","For","update","state",",","the","shapes","of","y_true","and","y_pred","are","expected","to","be",":","-","y_true",":","[","batch_size","x","1","]","array","of","0","for","negatives","and","1","for","positives","-","y_pred",":","[","batch_size","x","2","]","array","of","probabilities","where","y_pred","[",":",",0","]","are","the","probabilities","of","the","0th","(","negative",")","class","and","y_pred","[",":",",1","]","are","the","probabilities","of","the","1st","(","positive",")","class","See","https",":","\/\/www.tensorflow.org\/api_docs\/python\/tf\/keras\/metrics\/AUC","for","details","."]
code: def update_state(self, y_true, y_pred, sample_weight=None):
      super().update_state(y_true, y_pred[:, 1], sample_weight)
  
imports: import tensorflow
label_desc: Use image node_id 1 for calling the BinaryAUC obj's underlying member method code with example usage: obj.update_state(y_true, y_pred, sample_weight) without return types