class_docstr: Base class for the obfuscation datasets.
  
  This class provides the functionality required to run experiments with TPU
  support, via the input_fn function. Subclasses should implement the
  _read_dataset method, in order to read the input data from its respective
  source.
  
  Attributes:
    data_dir: Directory from which to read the data.
    split: Name of the split used (one of 'train' or 'test').
    batch_size: Batch size for the data.
    is_train: Whether the dataset is used for training or not.
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docstr: Abstract method that reads a dataset from the appropriate source.
  
  Args:
    context: A context object (tf.distribute.InputContext). If provided, this
      contains information about the TPU setup.
  
  Returns:
    A tf.data.Dataset, containing the data of the required shard of the
    dataset. The dataset returned should not be batched.
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code: def _read_dataset(
      self, context: Optional[tf.distribute.InputContext]
  ) -> tf.data.Dataset:
      """Abstract method that reads a dataset from the appropriate source.
  
      Args:
        context: A context object (tf.distribute.InputContext). If provided, this
          contains information about the TPU setup.
  
      Returns:
        A tf.data.Dataset, containing the data of the required shard of the
        dataset. The dataset returned should not be batched.
      """
  
imports: import abc
  from typing import Optional, Sequence, Tuple
  from meo.mlp_baseline import data_utils
  import tensorflow
  import tensorflow_datasets
label_desc: Use image node_id 2 for calling the ObfuscatedDatasetBase obj's underlying member method code with example usage: obj._read_dataset(context) without return types