class_docstr: Mapping from input to output embeddings using an MLP.
  
  This functions wraps a model that maps an embedding vector to another one in
  the same space (with the intent being to map obfuscated embeddings to clean
  ones). The mapping is performed using multi-layer perceptron.
  
  Attributes:
    mapping: Mapping MLP, taking obfuscated embeddings as input and
      returning clean ones.
class_docstr_tok: ["Mapping","from","input","to","output","embeddings","using","an","MLP",".","This","functions","wraps","a","model","that","maps","an","embedding","vector","to","another","one","in","the","same","space","(","with","the","intent","being","to","map","obfuscated","embeddings","to","clean","ones",")",".","The","mapping","is","performed","using","multi-layer","perceptron",".","Attributes",":","mapping",":","Mapping","MLP",",","taking","obfuscated","embeddings","as","input","and","returning","clean","ones","."]
code: def call(
      self, inputs: tf.Tensor
  ) -> tf.Tensor:  # pytype: disable=signature-mismatch
      return self.mapping(inputs)
  
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 MLPEmbeddingMapper obj's underlying member method code with example usage: obj.call(inputs) and returns: self