class_docstr: Callback for custom scoring methods.
docstr: Initializes the Callback.
  
  Args:
      summarizer: Summarizer that predicts over the validation data.
      scorers: Dictionary of {scorer_name: scorer}, where each scorer maps a prediction to a score.
      val_data: Raw validation data to predict on.
      print_num_examples: Number of prediction examples to output for eyeballing the prediction quality.
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code: def __init__(
      self,
      summarizer: Summarizer,
      scorers: Dict[str, Callable[[Dict], float]],
      val_data: Iterable[Tuple[str, str]],
      print_num_examples=5,
  ) -> None:
      """
      Initializes the Callback.
  
      Args:
          summarizer: Summarizer that predicts over the validation data.
          scorers: Dictionary of {scorer_name: scorer}, where each scorer maps a prediction to a score.
          val_data: Raw validation data to predict on.
          print_num_examples: Number of prediction examples to output for eyeballing the prediction quality.
      """
  
      super().__init__()
      self.summarizer = summarizer
      self.scorers = scorers
      self.val_data = val_data
      self.logger = get_logger(__name__)
      self.print_num_examples = print_num_examples
  
imports: from typing import Dict, Callable, Iterable, Tuple
  import tensorflow
  from headliner.model.summarizer import Summarizer
  from headliner.utils.logger import get_logger
label_desc: Use image node_id 1 to create a new EvaluationCallback object from inherited base classes: tf.keras.callbacks with example: obj = EvaluationCallback(summarizer, scorers, val_data, print_num_examples)