class_docstr: Compares two documents to solve a regression task.  
  
  :param config: A :py:class:`finetune.config.Settings` object or None (for default config).
  :param \**kwargs: key-value pairs of config items to override.
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docstr: :param pairs: Array of text, shape [batch_size, 2]
  :param Y: floating point targets
  :param batch_size: integer number of examples per batch. When N_GPUS > 1, this number
                     corresponds to the number of training examples provided to each GPU.
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code: def finetune(self, pairs, Y=None, context=None, **kwargs):
      """
      :param pairs: Array of text, shape [batch_size, 2]
      :param Y: floating point targets
      :param batch_size: integer number of examples per batch. When N_GPUS > 1, this number
                         corresponds to the number of training examples provided to each GPU.
      """
      return super().finetune(pairs, Y=Y, context=context, **kwargs)
  
imports: import tensorflow
  import numpy
  from finetune.encoding.target_encoders import RegressionEncoder
  from finetune.target_models.comparison import ComparisonPipeline
  from finetune.nn.target_blocks import regressor
  from finetune.base import BaseModel
  from finetune.model import PredictMode
label_desc: Use image node_id 7 for calling the ComparisonRegressor obj's underlying member method code with example usage: obj.finetune(pairs, Y, context) and returns: super