class_docstr: A no-op wrapper of datasets and signatures.
  
  Example usage:
  
  ```python
  train_dataset = tf.data.Dataset(...)
  valid_dataset = tf.data.Dataset(...)
  dataset_builder = NullDatasetBuilder(train_dataset, valid_dataset)
  ```
class_docstr_tok: ["A","no-op","wrapper","of","datasets","and","signatures",".","Example","usage",":","``","`","python","train_dataset","=","tf.data.Dataset","(","...",")","valid_dataset","=","tf.data.Dataset","(","...",")","dataset_builder","=","NullDatasetBuilder","(","train_dataset",",","valid_dataset",")","``","`"]
docstr: Initializes the instance.
  
  Args:
    train_dataset: A `tf.data.Dataset` for training.
    valid_dataset: A `tf.data.Dataset` for validation.
    signatures: A dict of signatures that formulate the model in functions
      that render the input data with given types. When None, no signatures
      assigned.
code: def __init__(self, train_dataset, valid_dataset, signatures=None):
      """Initializes the instance.
  
      Args:
        train_dataset: A `tf.data.Dataset` for training.
        valid_dataset: A `tf.data.Dataset` for validation.
        signatures: A dict of signatures that formulate the model in functions
          that render the input data with given types. When None, no signatures
          assigned.
      """
      self._train_dataset = train_dataset
      self._valid_dataset = valid_dataset
      self._signatures = signatures
  
imports: import abc
  import dataclasses
  import os
  from typing import Any, Dict, List, 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
  from tensorflow_ranking.python.keras import saved_model
  from tensorflow_ranking.python.keras import strategy_utils
label_desc: Use image node_id 1 to create a new NullDatasetBuilder object from inherited base classes: AbstractDatasetBuilder with example: obj = NullDatasetBuilder(train_dataset, valid_dataset, signatures)