class_docstr: Task object for TF-Ranking.
class_docstr_tok: ["Task","object","for","TF-Ranking","."]
code: def train_step(
      self,
      inputs,
      model: tf.keras.Model,
      optimizer: tf.keras.optimizers.Optimizer,
      metrics,
  ):
      if isinstance(inputs, tuple) and len(inputs) == 2:
          features, labels = inputs
      else:
          features, labels = inputs, inputs
      with tf.GradientTape() as tape:
          outputs = model(features, training=True)
          # Computes per-replica loss.
          loss = self.build_losses(
              labels=labels,
              model_outputs=outputs,
              aux_losses=model.losses,
          )
          scaled_loss = (
              loss / tf.distribute.get_strategy().num_replicas_in_sync
          )
      tvars = model.trainable_variables
      grads = tape.gradient(scaled_loss, tvars)
      optimizer.apply_gradients(list(zip(grads, tvars)))
      self.process_metrics(metrics, labels, outputs)
      return {self.loss: loss}
  
imports: import dataclasses
  from typing import Callable, Dict, Mapping, Optional, Tuple, Union
  import tensorflow
  from tensorflow_ranking.python import data
  from tensorflow_ranking.python.keras import losses
  from tensorflow_ranking.python.keras import metrics
  from tensorflow_ranking.python.keras import model
label_desc: Use image node_id 7 for calling the RankingTask obj's underlying member method code with example usage: obj.train_step(inputs, model, optimizer, metrics) and returns: dict