class_docstr: Transformer Model for Similarity between two strings.
  
  This model defines the architecture using two transformer
  networks, each of which embed a string and the loss is
  calculated as a Binary Cross-Entropy loss. Normalized
  Dot Product is used as the distance measure between two
  string embeddings.
class_docstr_tok: ["Transformer","Model","for","Similarity","between","two","strings",".","This","model","defines","the","architecture","using","two","transformer","networks",",","each","of","which","embed","a","string","and","the","loss","is","calculated","as","a","Binary","Cross-Entropy","loss",".","Normalized","Dot","Product","is","used","as","the","distance","measure","between","two","string","embeddings","."]
code: def infer(self, features=None, **kwargs):
      del kwargs
      predictions, _ = self(features)
      return predictions
  
file_path: tensor2tensor/tensor2tensor/models/research/similarity_transformer.py
imports: from tensor2tensor.data_generators import problem
  from tensor2tensor.layers import common_layers
  from tensor2tensor.models import transformer
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import t2t_model
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 4 for calling the SimilarityTransformer obj's underlying member method code with example usage: obj.infer(features) and returns: predictions