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 make_node(
      self,
      x,
      scale,
      bias,
      estimated_mean,
      estimated_variance,
      epsilon=1e-4,
  ):
      x = as_tensor_variable(x)
      scale = as_tensor_variable(scale)
      bias = as_tensor_variable(bias)
      estimated_mean = as_tensor_variable(estimated_mean)
      estimated_variance = as_tensor_variable(estimated_variance)
      epsilon = as_tensor_variable(epsilon)
      # Upcast to common dtype on the non-scalar
      # Keep as is dtype of scalar (epsilon)
      (
          x,
          scale,
          bias,
          estimated_mean,
          estimated_variance,
      ) = as_common_dtype(
          x, scale, bias, estimated_mean, estimated_variance
      )
      assert (
          x.ndim
          == scale.ndim
          == bias.ndim
          == estimated_mean.ndim
          == estimated_variance.ndim
      )
  
      return Apply(
          self,
          [x, scale, bias, estimated_mean, estimated_variance, epsilon],
          [x.type()],
      )
  
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 3 for calling the AbstractBatchNormInference obj's underlying member method code with example usage: obj.make_node(x, scale, bias, estimated_mean, estimated_variance, epsilon) and returns: Apply