code: def nonqnn_to_recording_resblock(
      x,
      cfg,
      maps,
      kernel=(3, 3),
      pad=(1, 1),
      stride=(1, 1),
      test=False,
      channel_last=False,
      bn_self_folding=False,
      record_layers=(),
      name="convblock",
  ):
      recorder = cfg.recorder_activation
      recorder_weight = cfg.recorder_weight
      axes = get_channel_axes(x, channel_last)
  
      with nn.parameter_scope(name):
          h = x
  
      with nn.parameter_scope("{}-conv".format(name)):
          h = recorder()(h, axes=axes)
          hr1 = h
          w, b = create_conv_weight_bias(
              h,
              maps=maps,
              kernel=kernel,
              channel_last=channel_last,
              name=name,
          )
          w = recorder_weight()(w, axes=axes, name="w")
          b = recorder_weight()(b, axes=axes, name="b")
          h = F.convolution(
              h, w, b, pad=pad, stride=stride, channel_last=channel_last
          )
      if bn_self_folding:
          a, b = create_scale_bias(1, h.shape, axes=axes)
  
          with nn.parameter_scope("{}-Mul2".format(name)):
              if not record_layers:
                  hr2 = recorder()(h, axes=axes)
                  h = recorder()(a, axes=axes) * hr2
              else:
                  h = a * h
  
          with nn.parameter_scope("{}-Add2".format(name)):
              if not record_layers:
                  hr3 = recorder()(h, axes=axes)
                  h = hr3 + recorder()(b, axes=axes)
              else:
                  h = h + b
      with nn.parameter_scope("{}/ReLU-Add2".format(name)):
          if not record_layers:
              hr2 = recorder()(h, axes=axes)
          else:
              hr2 = h
          h = hr2 + hr1
  
      with nn.parameter_scope("{}-ReLU".format(name)):
          if not record_layers:
              h = recorder()(h, axes=axes)
          else:
              h = h
      return F.relu(h)
  
imports: from __future__ import absolute_import
  import nnabla
  import nnabla.functions
  import nnabla.parametric_functions
  from .helper import create_scale_bias, create_conv_weight_bias, create_affine_weight_bias, get_channel_axes
label_desc: Use image node_id 36 for calling a global function with example usage: nonqnn_to_recording_resblock(x, cfg, maps, kernel, pad, stride, test, channel_last, bn__folding, record_layers, name) and returns: F