class_docstr: Classifies a document 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,
  ):
      return ordinal_regressor(
          hidden=featurizer_state["features"],
          targets=targets,
          n_targets=n_outputs,
          config=config,
          train=train,
          reuse=reuse,
          shared_threshold_weights=config.shared_threshold_weights,
          **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 8 for calling the OrdinalRegressor obj's underlying member method code with example usage: obj._target_model() and returns: ordinal_regressor