class_docstr: "Class to read a dataset of embeddings of obfuscated images.
  
  This class is used to read a tfrecord which contains the embeddings of the
  images directly. The dataset needs to contain two fields:
  - 'embed': The embeddings of the images. This field should contain a float
      vector of dimension (num_views * embed_dim), so it can then be reshaped to
      a 2D tensor.
  - 'label': The int label of the corresponding sample.
  
  Attributes:
    embed_dim: The dimension of the embeddings.
    num_views: Number of different views of each image to be found in the
      dataset (equal to the number of obfuscations + 1 for the clean image).
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code: def __init__(
      self,
      data_dir: str,
      embed_dim: int,
      num_views: int,
      batch_size: int,
      split: str,
  ):
      super().__init__(
          data_dir=data_dir, batch_size=batch_size, split=split
      )
      # Example filename: data_dir/<dataset name>_<split>.<suffix>
      self._filenames = sorted(
          tf.io.gfile.glob(self.data_dir + "/*{}*".format(split))
      )
      self.embed_dim = embed_dim
      self.num_views = num_views
  
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 1 to create a new ObfuscatedEmbeddingDataset object from inherited base classes: ObfuscatedDatasetBase with example: obj = ObfuscatedEmbeddingDataset(data_dir, embed_dim, num_views, batch_size, split)