class_docstr: Model that maps input embeddings to output embeddings in the same dimensionality.
  
  This is used as a template for classes implementing a mapping between
  embeddings. Classes that inherit this class must implement an appropriate call
  function.
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docstr: Abstract call function to be implemented by subclasses.
  
  Args:
    inputs: the input embeddings that the class must operate on.
  
  Returns:
    Either a single tensor which corresponds to the output embeddings, or a
    tuple of tensors, one of which is the output embedding and the rest are
    necessary components of the optimization process for each particular
    subclass.
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code: def call(
      self, inputs: tf.Tensor
  ) -> Union[
      tf.Tensor, Sequence[tf.Tensor]
  ]:  # pytype: disable=signature-mismatch
      """Abstract call function to be implemented by subclasses.
  
      Args:
        inputs: the input embeddings that the class must operate on.
  
      Returns:
        Either a single tensor which corresponds to the output embeddings, or a
        tuple of tensors, one of which is the output embedding and the rest are
        necessary components of the optimization process for each particular
        subclass.
      """
  
imports: import abc
  from typing import Union, Tuple, Sequence, Optional
  import tensorflow
  import tensorflow_hub
label_desc: Use image node_id 2 for calling the EmbeddingMapper obj's underlying member method code with example usage: obj.call(inputs) without return types