code: def nn_predict(params, inputs, nonlinearity=np.tanh):
      for W, b in params:
          outputs = np.dot(inputs, W) + b
          inputs = nonlinearity(outputs)
      return outputs
  
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 2 for calling a global function with example usage: nn_predict(params, inputs, nonlinearity) and returns: outputs