code: def nonqnn_to_specific_recording_pos_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
          )
          h = recorder()(h, axes=axes)
  
          h = h + hr1
  
      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 42 for calling a global function with example usage: nonqnn_to_specific_recording_pos_resblock(x, cfg, maps, kernel, pad, stride, test, channel_last, bn__folding, record_layers, name) and returns: F