code: def sigmoid(x, w):
      """sigmoid(w^T x)
      To suppress the warnings at np.exp, do "np.seterr(all='ignore')"
  
      Parameters
      ----------
      x : array, shape=(d)
          input vector
      w : array, shape=(d)
          weight
  
      -------
      sigmoid : float
          sigmoid(w^T x)
      """
  
      s = np.clip(np.dot(w, x), -SIGMOID_RANGE, SIGMOID_RANGE)
  
      return 1.0 / (1.0 + np.exp(-s))
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import unicode_literals
  import logging
  import numpy
  from scipy.optimize import fmin_cg
  from sklearn.linear_model import LogisticRegression
  from sklearn.base import BaseEstimator, ClassifierMixin
label_desc: Use image node_id 1 for calling a global function with example usage: sigmoid(x, w) and returns: unknown