code: def res_block(
      nf,
      dense: bool = False,
      norm_type="Batch",
      bottle: bool = False,
      **conv_kwargs,
  ):
      "Resnet block of `nf` features. `conv_kwargs` are passed to `conv_layer`."
      norm2 = norm_type
      if not dense and (norm_type == "Batch"):
          norm2 = "BatchZero"
      nf_inner = nf // 2 if bottle else nf
      return SequentialEx(
          conv_layer(nf, nf_inner, norm_type=norm_type, **conv_kwargs),
          conv_layer(nf_inner, nf, norm_type=norm2, **conv_kwargs),
          MergeLayer(dense),
      )
  
imports: import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle.vision.models import resnet101
  from .None import utils
label_desc: Use image node_id 5 for calling a global function with example usage: res_block(nf, dense, norm_type, bottle) and returns: SequentialEx