code: def ctc(activations, labels, input_lengths):
      """
      Compute CTC loss function.
  
      Notes
      -----
      Using the loss function requires that the Baidu's warp-ctc library be installed.
      If the warp-ctc library is not on the compiler's default library path, the
      configuration variable ``config.ctc.root`` must be properly set.
  
      Parameters
      ----------
      activations
          Three-dimensional tensor, which has a shape of (t, m, p), where
          t is the time index, m is the minibatch index, and p is the index
          over the probabilities of each symbol in the alphabet. The memory
          layout is assumed to be in C-order, which consists in the slowest
          to the fastest changing dimension, from left to right. In this case,
          p is the fastest changing dimension.
      labels
          A 2-D tensor of all the labels for the minibatch. In each row, there
          is a sequence of target labels. Negative values are assumed to be padding,
          and thus are ignored. Blank symbol is assumed to have index 0 in the
          alphabet.
      input_lengths
          A 1-D tensor with the number of time steps for each sequence in
          the minibatch.
  
      Returns
      -------
      1-D array
          Cost of each example in the minibatch.
      """
      return ConnectionistTemporalClassification()(
          activations, labels, input_lengths
      )
  
imports: from __future__ import division, absolute_import, print_function
  import os
  import sys
  import theano.tensor
  from theano import config
  from theano import gof
  from theano.gof import local_optimizer
  from theano.gof.cmodule import GCC_compiler
  from theano.tensor.opt import register_canonicalize
  from theano.tensor.extra_ops import cpu_contiguous
  from theano.gradient import grad_undefined
label_desc: Use image node_id 5 for calling a global function with example usage: ctc(activations, labels, input_lengths) and returns: ConnectionistTemporalClassification