code: def _block2(
      x,
      filters,
      kernel_size=3,
      stride=1,
      conv_shortcut=True,
      scope=None,
  ):
      if conv_shortcut is True:
          shortcut = conv(x, 4 * filters, 1, stride=stride, scope="0")
      else:
          shortcut = x
      x = batch_norm(x)
      x = relu(x)
      x = conv(x, filters, 1, stride=1, scope="1")
      x = relu(x, name="1/relu")
      x = pad(x, pad_info(kernel_size), name="2/pad")
      x = conv(x, filters, kernel_size, stride=stride, scope="2")
      x = relu(x, name="2/relu")
      x = conv2d(x, 4 * filters, 1, stride=1, scope="3/conv")
      x = add(shortcut, x, name="out")
      return x
  
imports: from __future__ import absolute_import
  from __future__ import division
  import tensorflow
  from .layers import batch_norm
  from .layers import conv2d
  from .layers import fc
  from .layers import max_pool2d
  from .layers import sconv2d
  from .layers import convbn
  from .layers import gconvbn
  from .ops import *
  from .utils import pad_info
  from .utils import set_args
  from .utils import var_scope
label_desc: Use image node_id 17 for calling a global function with example usage: _block2(x, filters, kernel_size, stride, conv_shortcut, scope) and returns: x