class_docstr: Compares two documents and classifies into two or more ordered categories.
  
  For a full list of configuration options, see `finetune.config`.
  
  :param config: A config object generated by `finetune.config.get_config` or None (for default config).
  :param \**kwargs: key-value pairs of config items to override.
code: def _target_model(
      self,
      *,
      config,
      featurizer_state,
      targets,
      n_outputs,
      train=False,
      reuse=None,
      **kwargs,
  ):
      featurizer_state["sequence_features"] = tf.abs(
          tf.reduce_sum(
              input_tensor=featurizer_state["sequence_features"], axis=1
          )
      )
      featurizer_state["features"] = tf.abs(
          tf.reduce_sum(
              input_tensor=featurizer_state["features"], axis=1
          )
      )
      if "context" in featurizer_state:
          featurizer_state["context"] = tf.abs(
              tf.reduce_sum(
                  input_tensor=featurizer_state["context"], axis=1
              )
          )
      return super(ComparisonOrdinalRegressor, self)._target_model(
          config=config,
          featurizer_state=featurizer_state,
          targets=targets,
          n_outputs=n_outputs,
          train=train,
          reuse=reuse,
          **kwargs,
      )
  
imports: import tensorflow
  import numpy
  from scipy.stats import mode
  from finetune.base import BaseModel
  from finetune.encoding.target_encoders import OrdinalRegressionEncoder
  from finetune.nn.target_blocks import ordinal_regressor
  from finetune.input_pipeline import BasePipeline
  from finetune.target_models.comparison_regressor import ComparisonRegressionPipeline
  from finetune.target_models.comparison import Comparison
label_desc: Use image node_id 3 for calling the ComparisonOrdinalRegressor obj's underlying member method code with example usage: obj._target_model() and returns: super