class_docstr: GPU wrapper for Baidu CTC loss function.
  
  Parameters
  ----------
  compute_grad
      If set to True, enables the computation of gradients of the CTC loss function.
class_docstr_tok: ["GPU","wrapper","for","Baidu","CTC","loss","function",".","Parameters","--","--","--","--","--","compute_grad","If","set","to","True",",","enables","the","computation","of","gradients","of","the","CTC","loss","function","."]
code: def make_node(self, activations, labels, input_lengths):
      context_name = infer_context_name(activations)
      t_activations = as_gpuarray_variable(
          activations, context_name=context_name
      )
      # Ensure activations array is C-contiguous
      t_activations = gpu_contiguous(t_activations)
  
      # Labels and input lengths are always on the CPU
      t_labels = T.as_tensor_variable(labels)
      t_input_lengths = T.as_tensor_variable(input_lengths)
  
      if t_activations.type.dtype != "float32":
          raise TypeError("activations must use the float32 type.")
  
      if t_activations.ndim != 3:
          raise ValueError("activations must have 3 dimensions.")
  
      if t_labels.type.dtype != "int32":
          raise TypeError("labels must use the int32 type.")
  
      if t_labels.ndim != 2:
          raise ValueError("labels must have 2 dimensions.")
  
      if t_input_lengths.type.dtype != "int32":
          raise TypeError("input_lengths must use the int32 type.")
  
      if t_input_lengths.ndim != 1:
          raise ValueError("input_lengths must have 1 dimension.")
  
      costs = GpuArrayType(
          dtype="float32",
          broadcastable=(False,),
          context_name=context_name,
      )()
      outputs = [costs]
  
      if self.compute_grad:
          gradients = GpuArrayType(
              dtype="float32",
              broadcastable=(
                  False,
                  False,
                  False,
              ),
              context_name=context_name,
          )()
          outputs += [gradients]
  
      return theano.Apply(
          self,
          inputs=[t_activations, t_labels, t_input_lengths],
          outputs=outputs,
      )
  
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 8 for calling the GpuConnectionistTemporalClassification obj's underlying member method code with example usage: obj.make_node(activations, labels, input_lengths) and returns: theano