class_docstr: A distance between neural networks based on solving a transportation problem. 
docstr: Sets up the connectivity difference cost function. 
docstr_tok: ["Sets","up","the","connectivity","difference","cost","function","."]
code: def _set_up_connectivity_cost_function(
      self, connectivity_diff_cost_function
  ):
      """Sets up the connectivity difference cost function."""
      self.connectivity_diff_cost_function = (
          connectivity_diff_cost_function
      )
      if connectivity_diff_cost_function == "linear":
          self._conn_diff_cost_func = np.abs
      elif connectivity_diff_cost_function == "sqrt":
          self._conn_diff_cost_func = lambda x: np.sqrt(np.abs(x))
      elif connectivity_diff_cost_function == "log":
          self._conn_diff_cost_func = lambda x: np.log(np.abs(x))
      elif connectivity_diff_cost_function.startswith("poly"):
          poly_order = float(connectivity_diff_cost_function[4:])
          self._conn_diff_cost_func = lambda x: np.abs(x) ** poly_order
      return
  
imports: import numpy
  from .None import neural_network
  from ..gp.kernel import ExpSumOfDistsKernel, SumOfExpSumOfDistsKernel
  from ..utils.oper_utils import opt_transport
label_desc: Use image node_id 2 for calling the OTMANNDistanceComputer obj's underlying member method code with example usage: obj._set_up_connectivity_cost_function(connectivity_diff_cost_function) without return types