code: def _block1(
      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
      # Most reference implementations (e.g., TF-slim and Torch-ResNets)
      # apply a stride of 2 on the 3x3 conv kernel like the below `_block2`,
      # but here the stride 2 on the 1x1 to follow the original Caffe-ResNets.
      x = conv(x, filters, 1, stride=stride, scope="1")
      x = relu(x, name="1/relu")
      x = conv(
          x, filters, kernel_size, stride=1, padding="SAME", scope="2"
      )
      x = relu(x, name="2/relu")
      x = conv(x, 4 * filters, 1, stride=1, scope="3")
      x = relu(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 16 for calling a global function with example usage: _block1(x, filters, kernel_size, stride, conv_shortcut, scope) and returns: x