class_docstr: Abstract Op for Batch Normalization.
  
  Parameters
  ----------
  axes : a tuple of ints
      The axes along which the input is normalized.
  epsilon
      Epsilon value used in the batch normalization formula. Minimum allowed
      value is 1e-5 (imposed by cuDNN).
class_docstr_tok: ["Abstract","Op","for","Batch","Normalization",".","Parameters","--","--","--","--","--","axes",":","a","tuple","of","ints","The","axes","along","which","the","input","is","normalized",".","epsilon","Epsilon","value","used","in","the","batch","normalization","formula",".","Minimum","allowed","value","is","1e-5","(","imposed","by","cuDNN",")","."]
code: def connection_pattern(self, node):
      # Specificy that epsilon is not connected to outputs.
      return [[True], [True], [True], [True], [True], [False]]
  
imports: from __future__ import absolute_import, print_function, division
  import numpy
  import theano
  from theano import Apply, Op
  from theano.gof import local_optimizer
  from theano.gof.opt import copy_stack_trace
  from theano.tensor import as_tensor_variable, TensorType
  from theano.tensor import basic
  from theano.tensor.opt import register_specialize_device
  from theano.scalar import Composite, as_common_dtype
  from theano.scalar import add, sub, true_div, mul
label_desc: Use image node_id 5 for calling the AbstractBatchNormInference obj's underlying member method code with example usage: obj.connection_pattern(node) and returns: list