code: def gpu_ctc(activations, labels, input_lengths):
      """
      Compute CTC loss function on the GPU.
  
      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 GpuConnectionistTemporalClassification()(
          activations, labels, input_lengths
      )
  
imports: from __future__ import absolute_import, print_function, division
  import theano
  from theano import config, gof
  import theano.tensor
  from .basic_ops import gpu_contiguous, as_gpuarray_variable, infer_context_name, gpuarray_helper_inc_dir
  import theano.tensor.nnet.ctc
  from .type import GpuArrayType, gpu_context_type
  from .elemwise import GpuDimShuffle
  from theano.gradient import grad_undefined
  from theano.gof import local_optimizer
  from theano.tensor.opt import register_canonicalize
  from theano.tensor.nnet.ctc import ctc_available
  import os
  import sys
  from .None import pygpu
label_desc: Use image node_id 1 for calling a global function with example usage: gpu_ctc(activations, labels, input_lengths) and returns: GpuConnectionistTemporalClassification