class_docstr: Builds datasets from feature specs with a single label spec.
  
  This supports a single dataset with a single label, which is supposed to be a
  dense Tensor.
  
  Example usage:
  
  ```python
  context_feature_spec = {}
  example_feature_spec = {
      "example_feature_1": tf.io.FixedLenFeature(
          shape=(1,), dtype=tf.float32, default_value=0.0)
  }
  mask_feature_name = "list_mask"
  label_spec = {
      "utility": tf.io.FixedLenFeature(
          shape=(1,), dtype=tf.float32, default_value=0.0)
  }
  dataset_hparams = DatasetHparams(
      train_input_pattern="train.dat",
      valid_input_pattern="valid.dat",
      train_batch_size=128,
      valid_batch_size=128)
  dataset_builder = SimpleDatasetBuilder(
      context_feature_spec,
      example_feature_spec,
      mask_feature_name,
      label_spec,
      dataset_hparams)
  ```
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docstr: Intializes the instance.
  
  Args:
    context_feature_spec: Maps context (aka, query) names to feature specs.
    example_feature_spec: Maps example (aka, document) names to feature specs.
    mask_feature_name: If set, populates the feature dictionary with this name
      and the coresponding value is a `tf.bool` Tensor of shape [batch_size,
      list_size] indicating the actual example is padded or not.
    label_spec: A tuple of the label name and a tf.io.FixedLenFeature spec, or
      a dict that maps task name to label spec in multi-task setting.
    hparams: A dict containing model hyperparameters.
    sample_weight_spec: Feature spec for per-example weight.
code: def __init__(
      self,
      context_feature_spec: Dict[
          str,
          Union[
              tf.io.FixedLenFeature,
              tf.io.VarLenFeature,
              tf.io.RaggedFeature,
          ],
      ],
      example_feature_spec: Dict[
          str,
          Union[
              tf.io.FixedLenFeature,
              tf.io.VarLenFeature,
              tf.io.RaggedFeature,
          ],
      ],
      mask_feature_name: str,
      label_spec: Tuple[str, tf.io.FixedLenFeature],
      hparams: DatasetHparams,
      sample_weight_spec: Optional[
          Tuple[str, tf.io.FixedLenFeature]
      ] = None,
  ):
      """Intializes the instance.
  
      Args:
        context_feature_spec: Maps context (aka, query) names to feature specs.
        example_feature_spec: Maps example (aka, document) names to feature specs.
        mask_feature_name: If set, populates the feature dictionary with this name
          and the coresponding value is a `tf.bool` Tensor of shape [batch_size,
          list_size] indicating the actual example is padded or not.
        label_spec: A tuple of the label name and a tf.io.FixedLenFeature spec, or
          a dict that maps task name to label spec in multi-task setting.
        hparams: A dict containing model hyperparameters.
        sample_weight_spec: Feature spec for per-example weight.
      """
      super().__init__(
          context_feature_spec=context_feature_spec,
          example_feature_spec=example_feature_spec,
          training_only_example_spec=dict(
              [label_spec]
              + ([sample_weight_spec] if sample_weight_spec else [])
          ),
          mask_feature_name=mask_feature_name,
          hparams=hparams,
      )
      self._label_spec = label_spec
      self._sample_weight_spec = sample_weight_spec
  
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 SimpleDatasetBuilder object from inherited base classes: BaseDatasetBuilder with example: obj = SimpleDatasetBuilder(context_feature_spec, example_feature_spec, mask_feature_name, label_spec, hparams, sample_weight_spec)