class_docstr: Task object for tf-ranking BERT.
class_docstr_tok: ["Task","object","for","tf-ranking","BERT","."]
code: def validation_step(
      self, inputs, model: tf.keras.Model, metrics=None
  ):
      if isinstance(inputs, tuple) and len(inputs) == 2:
          features, labels = inputs
      else:
          features, labels = inputs, inputs
      outputs = self.inference_step(features, model)
      loss = self.build_losses(
          labels=labels, model_outputs=outputs, aux_losses=model.losses
      )
      logs = {self.loss: loss}
      if metrics:
          self.process_metrics(metrics, labels, outputs)
      logs.update({_PREDICTION: outputs, _LABEL: labels})
      # Collect extra feature values like query ids and document ids.
      # Notice that only 1-d features are supported.
      logging_feature_names = self._get_logging_feature_names()
      logs.update(
          {
              feature_name: tf.squeeze(
                  features[feature_name],
                  axis=list(
                      range(2, len(tf.shape(features[feature_name])))
                  ),
              )
              for feature_name in logging_feature_names
          }
      )
      return logs
  
imports: import dataclasses
  import os
  import time
  from typing import Dict, Mapping, Optional, Tuple
  from absl import logging
  import numpy
  import tensorflow
  from official.core import task_factory
  from official.modeling.hyperparams import base_config
  from official.nlp.configs import encoders
  from tensorflow_ranking.extension import task
  from tensorflow_ranking.python.keras import metrics
  from tensorflow_ranking.python.keras import model
label_desc: Use image node_id 4 for calling the TFRBertTask obj's underlying member method code with example usage: obj.validation_step(inputs, model, metrics) and returns: logs