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: Produces a list of most likely class labels as determined by the fine-tuned model.
  
  :param X: list or array of text to embed.
  :returns: list of class labels.
docstr_tok: ["Produces","a","list","of","most","likely","class","labels","as","determined","by","the","fine-tuned","model",".",":","param","X",":","list","or","array","of","text","to","embed",".",":","returns",":","list","of","class","labels","."]
code: def predict(self, X, context=None, **kwargs):
      """
      Produces a list of most likely class labels as determined by the fine-tuned model.
  
      :param X: list or array of text to embed.
      :returns: list of class labels.
      """
      return super().predict(X, context=context, **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 4 for calling the OrdinalRegressor obj's underlying member method code with example usage: obj.predict(X, context) and returns: super