class_docstr: Params needed for saving the necessary information during export.
class_docstr_tok: ["Params","needed","for","saving","the","necessary","information","during","export","."]
docstr: Constructor of _BagOfSparseFeaturesEmbeddingParams.
  
  Args:
    embedding_dim: An integer, the embedding dimension for the
      informative part of the input. It is also the embedding dimension for
      the output.
    sigma_dim: An integer, the variable size for computing the \sigma(x)
      function for each input x.
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code: def __init__(self, embedding_dim: int, sigma_dim: int):
      r"""Constructor of _BagOfSparseFeaturesEmbeddingParams.
  
      Args:
        embedding_dim: An integer, the embedding dimension for the
          informative part of the input. It is also the embedding dimension for
          the output.
        sigma_dim: An integer, the variable size for computing the \sigma(x)
          function for each input x.
      """
      self.embedding_dim = embedding_dim
      self.sigma_dim = sigma_dim
  
imports: import collections
  import threading
  import typing
  from research.carls import dynamic_embedding_config_pb2
  from research.carls import dynamic_embedding_ops
  from six.moves import range
  import tensorflow
label_desc: Use image node_id 1 to create a new _BagOfSparseFeaturesEmbeddingParams object from inherited base classes: object with example: obj = _BagOfSparseFeaturesEmbeddingParams(embedding_dim, sigma_dim)