class_docstr: The original batch normalization paper includes two learned
  parameters: a scaling factor \gamma and a bias eta.
  Caffe's implementation does not include these two. However, it is commonly
  replicated by adding a scaling+bias layer immidiately after the batch norm.
  
  This fuser merges the scaling+bias layer with the batch norm.
class_docstr_tok: ["The","original","batch","normalization","paper","includes","two","learned","parameters",":","a","scaling","factor","\\gamma","and","a","bias","\beta",".","Caffe","'s","implementation","does","not","include","these","two",".","However",",","it","is","commonly","replicated","by","adding","a","scaling+bias","layer","immidiately","after","the","batch","norm",".","This","fuser","merges","the","scaling+bias","layer","with","the","batch","norm","."]
code: def merge(self, parent, child):
      parent.scale_bias_node = child
  
imports: from __future__ import unicode_literals
  from google.protobuf import text_format
  import numpy
  from mmdnn.conversion.caffe.graph import GraphBuilder, NodeKind, LAYER_IN_TRAIN_PROTO
  from mmdnn.conversion.caffe.mapper import NodeMapper, get_handler_name
  from mmdnn.conversion.caffe.resolver import get_caffe_resolver, has_pycaffe
  from mmdnn.conversion.caffe.errors import print_stderr, ConversionError
  from mmdnn.conversion.caffe.common_graph import Graph
  from mmdnn.conversion.caffe.utils import get_lower_case, get_upper_case
label_desc: Use image node_id 2 for calling the BatchNormScaleBiasFuser obj's underlying member method code with example usage: obj.merge(parent, child) without return types