class_docstr: Fuse the following ``batch_norm`` layer into ``conv`` and ``conv_transpose``.
  That is, convert ``conv + batch_norm`` to ``conv``, by modifying the weight and bias in the ``conv`` layer.
  
  .. code-block::
  
      Given:
          %2 = conv(%1)
          ...
          %3 = batch_norm(%2)
          ...
  
      Result:
          %3 = conv(%1)
          ...
class_docstr_tok: ["Fuse","the","following","``","batch_norm","``","layer","into","``","conv","``","and","``","conv_transpose","``",".","That","is",",","convert","``","conv","+","batch_norm","``","to","``","conv","``",",","by","modifying","the","weight","and","bias","in","the","``","conv","``","layer",".","..","code-block",":",":","Given",":","%","2","=","conv","(","%","1",")","...","%","3","=","batch_norm","(","%","2",")","...","Result",":","%","3","=","conv","(","%","1",")","..."]
code: def apply(self, prog):
      for f in prog.functions.values():
          block_changed = True
          while block_changed:
              block_changed = self._fuse_conv_batchnorm_block(f)
  
imports: import copy
  import numpy
  from coremltools import _logger
  from coremltools.converters.mil.mil import Block
  from coremltools.converters.mil.mil import Builder
  from coremltools.converters.mil.mil import Operation, types
  from coremltools.converters.mil.mil.passes.graph_pass import AbstractGraphPass
  from coremltools.converters.mil.mil.passes.helper import _check_child_op_type, _check_no_output_connection, block_context_manager
  from coremltools.converters.mil.mil.passes.pass_registry import register_pass
  from coremltools.converters.mil.mil.types.symbolic import any_symbolic
label_desc: Use image node_id 1 for calling the fuse_conv_batchnorm obj's underlying member method code with example usage: obj.apply(prog) without return types