code: def inplace_logistic_derivative(Z, delta):
      """Apply the derivative of the logistic sigmoid function.
  
      It exploits the fact that the derivative is a simple function of the output
      value from logistic function.
  
      Parameters
      ----------
      Z : {array-like, sparse matrix}, shape (n_samples, n_features)
          The data which was output from the logistic activation function during
          the forward pass.
  
      delta : {array-like}, shape (n_samples, n_features)
           The backpropagated error signal to be modified inplace.
      """
      delta *= Z
      delta *= 1 - Z
  
file_path: catboost/contrib/python/scikit-learn/py2/sklearn/neural_network/_base.py
imports: import numpy
  from ..utils.fixes import expit
label_desc: Use image node_id 7 for calling a global function with example usage: inplace_logistic_derivative(Z, delta) without return types