parameters: (tparams, options)
return_statement: return [x, mask, im], cost
docstring: Computation graph for the entire model
docstring_summary: Computation graph for the entire model
docstring_tokens: Computation graph for the entire model
function: def build_model(tparams, options):
      """
      Computation graph for the entire model
      """
      x = tensor.matrix('x', dtype='int64')
      mask = tensor.matrix('mask', dtype='float32')
      im = tensor.matrix('im', dtype='float32')
  
      # embed sentences and images
      s_emb = encode_sentences(tparams, options, x, mask)
      im_emb = encode_images(tparams, options, im)
  
      # Compute loss
      cost = contrastive_loss(s_emb, im_emb, options)
  
      return [x, mask, im], cost
function_tokens: def build_model ( tparams , options ) : x = tensor . matrix ( 'x' , dtype = 'int64' ) mask = tensor . matrix ( 'mask' , dtype = 'float32' ) im = tensor . matrix ( 'im' , dtype = 'float32' ) # embed sentences and images s_emb = encode_sentences ( tparams , options , x , mask ) im_emb = encode_images ( tparams , options , im ) # Compute loss cost = contrastive_loss ( s_emb , im_emb , options ) return [ x , mask , im ] , cost