code: def neural_net_predict(params, inputs):
      """Implements a deep neural network for classification.
      params is a list of (weights, bias) tuples.
      inputs is an (N x D) matrix.
      returns normalized class log-probabilities."""
      for W, b in params:
          outputs = np.dot(inputs, W) + b
          inputs = np.tanh(outputs)
      return outputs - logsumexp(outputs, axis=1, keepdims=True)
  
file_path: autograd/examples/neural_net.py
imports: from __future__ import absolute_import, division
  from __future__ import print_function
  import autograd.numpy
  import autograd.numpy.random
  from autograd.scipy.special import logsumexp
  from autograd import grad
  from autograd.misc.flatten import flatten
  from autograd.misc.optimizers import adam
  from data import load_mnist
label_desc: Use image node_id 2 for calling a global function with example usage: neural_net_predict(params, inputs) and returns: unknown