code: def bsf_resblock(
      x,
      maps,
      kernel=(3, 3),
      pad=(1, 1),
      stride=(1, 1),
      test=False,
      w_bias=False,
      name="convblock",
  ):
      with nn.parameter_scope(name):
          h = PF.convolution(
              x,
              maps,
              kernel=kernel,
              pad=pad,
              stride=stride,
              with_bias=w_bias,
          )
          h = PF.batch_normalization(h, batch_stat=False)
      return F.relu(h + x)
  
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 19 for calling a global function with example usage: bsf_resblock(x, maps, kernel, pad, stride, test, w_bias, name) and returns: F