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 c_compile_args(self):
      if ctc_available.path is not None:
          if sys.platform != "darwin" and " " in ctc_available.path:
              return ['-Wl,-rpath,"' + ctc_available.path + '"']
          else:
              return ["-Wl,-rpath," + ctc_available.path]
      return []
  
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 3 for calling the GpuConnectionistTemporalClassification obj's underlying member method code with example usage: obj.c_compile_args() and returns: list, list, list