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.
docstr: Produces a floating point prediction determined by the fine-tuned model.
  
  
  :param pairs: Array of text, shape [batch, 2]
  :returns: list of floats, shape [batch]
docstr_tok: ["Produces","a","floating","point","prediction","determined","by","the","fine-tuned","model",".",":","param","pairs",":","Array","of","text",",","shape","[","batch",",","2","]",":","returns",":","list","of","floats",",","shape","[","batch","]"]
code: def predict(self, pairs, **kwargs):
      """
      Produces a floating point prediction determined by the fine-tuned model.
  
  
      :param pairs: Array of text, shape [batch, 2]
      :returns: list of floats, shape [batch]
      """
      return list(Comparison.predict(self, pairs, **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 1 for calling the ComparisonOrdinalRegressor obj's underlying member method code with example usage: obj.predict(pairs) and returns: list