code: def logprob(weights, inputs, targets, noise_scale=0.1):
      predictions = nn_predict(weights, inputs)
      return np.sum(norm.logpdf(predictions, targets, noise_scale))
  
file_path: autograd/examples/neural_net_regression.py
imports: from __future__ import absolute_import
  from __future__ import print_function
  import matplotlib.pyplot
  import autograd.numpy
  import autograd.numpy.random
  import autograd.scipy.stats.norm
  from autograd import grad
  from autograd.misc import flatten
  from autograd.misc.optimizers import adam
label_desc: Use image node_id 4 for calling a global function with example usage: logprob(weights, inputs, targets, noise_scale) and returns: np