code: def _human_and(X, model_generator, method_name, fever, cough):
      assert (
          np.abs(X).max() == 0
      ), "Human agreement metrics are only for use with the human_agreement dataset!"
  
      # these are from the sickness_score mturk user study experiment
      X_test = np.zeros((100, 3))
      if not fever and not cough:
          human_consensus = np.array([0.0, 0.0, 0.0])
          X_test[0, :] = np.array([[0.0, 0.0, 1.0]])
      elif not fever and cough:
          human_consensus = np.array([0.0, 2.0, 0.0])
          X_test[0, :] = np.array([[0.0, 1.0, 1.0]])
      elif fever and cough:
          human_consensus = np.array([5.0, 5.0, 0.0])
          X_test[0, :] = np.array([[1.0, 1.0, 1.0]])
  
      # force the model to fit an XOR function with almost entirely zero background
      model = _fit_human(model_generator, 0, 2, 10)
  
      attr_function = getattr(methods, method_name)(model, X)
      methods_attrs = attr_function(X_test)
      return "human", (human_consensus, methods_attrs[0, :])
  
imports: import hashlib
  import os
  import time
  import numpy
  import sklearn
  from ..None import __version__
  from .None import measures, methods
label_desc: Use image node_id 45 for calling a global function with example usage: _human_and(X, model_generator, method_name, fever, cough) and returns: str,  human_consensus,  methods_attrs