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.
docstr: Embeds inputs in learned feature space. Can be called before or after calling :meth:`finetune`.
  
  :param X: list or array of text to embed.
  :returns: np.array of features of shape (n_examples, embedding_size).
docstr_tok: ["Embeds","inputs","in","learned","feature","space",".","Can","be","called","before","or","after","calling",":","meth",":","`","finetune","`",".",":","param","X",":","list","or","array","of","text","to","embed",".",":","returns",":","np.array","of","features","of","shape","(","n_examples",",","embedding_size",")","."]
code: def featurize(self, X, **kwargs):
      """
      Embeds inputs in learned feature space. Can be called before or after calling :meth:`finetune`.
  
      :param X: list or array of text to embed.
      :returns: np.array of features of shape (n_examples, embedding_size).
      """
      return self._featurize(X, **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 OrdinalRegressor obj's underlying member method code with example usage: obj.featurize(X) and returns: self