code: def unet_3x_with_res_in_mid(feat_in, out_filters, norm2d):
      """Helper function of a Unet with res blocks in the middle."""
      e1 = layer_utils.residual_block_2d(
          feat_in, nfilters=128, strides=(2, 2), normalization=norm2d
      )  # 16x128
      e2 = layer_utils.residual_block_2d(
          e1, nfilters=256, strides=(2, 2), normalization=norm2d
      )  # 8x256
      e3 = layer_utils.residual_block_2d(
          e2, nfilters=512, strides=(2, 2), normalization=norm2d
      )  # 4x512
  
      mid1 = layer_utils.residual_block_2d(
          e3, nfilters=512, strides=(1, 1), normalization=norm2d
      )  # 32x32xnf_2d
      mid2 = layer_utils.residual_block_2d(
          mid1, nfilters=512, strides=(1, 1), normalization=norm2d
      )  # 32x32xnf_2d
      mid3 = layer_utils.residual_block_2d(
          mid2, nfilters=512, strides=(1, 1), normalization=norm2d
      )  # 32x32xnf_2d
      d0 = layer_utils.upconv(
          mid3, nfilters=256, size=4, strides=1
      )  # 8x256
      d1 = layers.concatenate([d0, e2])  # 8x512
      d2 = layers.Conv2D(
          256,
          kernel_size=4,
          strides=(1, 1),
          padding="same",
          kernel_initializer=initializer,
      )(
          d1
      )  # 8x256
  
      d3 = layer_utils.upconv(
          d2, nfilters=128, size=4, strides=1
      )  # 16x128
      d4 = layers.concatenate([d3, e1])  # 16x256
      d5 = layers.Conv2D(
          128,
          kernel_size=4,
          strides=(1, 1),
          padding="same",
          kernel_initializer=initializer,
      )(
          d4
      )  # 8x256
  
      d6 = layer_utils.upconv(
          d5, nfilters=64, size=4, strides=1
      )  # 32x64
      d7 = layers.concatenate([d6, feat_in])  # 32xN
      d8 = layers.Conv2D(
          out_filters,
          kernel_size=4,
          strides=(1, 1),
          padding="same",
          kernel_initializer=initializer,
      )(
          d7
      )  # 32xout
  
      return d8
  
imports: import tensorflow.compat.v1
  import tensorflow_graphics.projects.neural_voxel_renderer.layers
label_desc: Use image node_id 1 for calling a global function with example usage: unet_3x_with_res_in_mid(feat_in, out_filters, norm2d) and returns: d8