code: def evaluate_model(
      video_eval_dataset, model, num_batches, batch_size
  ):
      """Computes the FVD video metric.
  
      Args:
        video_eval_dataset: VideoEvaluationDataset tuple with video and frames
          information.
        model: Model tuple with model to evaluate.
        num_batches: number of batches to evaluate.
        batch_size: number of videos to compute per batch.
  
      Returns:
        FVD metric (float).
      """
      del video_eval_dataset, model, num_batches, batch_size
  
file_path: tensor2tensor/tensor2tensor/metrics/video_conditional_fvd.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
label_desc: Use image node_id 1 for calling a global function with example usage: evaluate_model(video_eval_dataset, model, num_batches, batch_size) without return types