code: def body(self, features):
      target_modality = self._problem_hparams.modality["targets"]
      # If we're just predicting a class, there is no use for a decoder.
      run_decoder = (
          target_modality != modalities.ModalityType.CLASS_LABEL
      )
      return slicenet_internal(
          features["inputs"],
          features["targets"],
          features["target_space_id"],
          self._hparams,
          run_decoder=run_decoder,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from six.moves import range
  from six.moves import zip
  from tensor2tensor.layers import common_attention
  from tensor2tensor.layers import common_hparams
  from tensor2tensor.layers import common_layers
  from tensor2tensor.layers import modalities
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import t2t_model
  import tensorflow.compat.v1
label_desc: Use image node_id 1 for calling the SliceNet obj's underlying member method code with example usage: obj.body(features) and returns: slicenet_internal