parameters: (prefix, config, init_dict, network, input_tensor)
return_statement: return OUT
docstring: Create the squad output
docstring_summary: Create the squad output
docstring_tokens: Create the squad output
function: def squad_output(prefix, config, init_dict, network, input_tensor):
      """
      Create the squad output
      """
      hidden_size = config.hidden_size
  
      W_out = init_dict[prefix + SQD_W]
      B_out = init_dict[prefix + SQD_B]
  
      if config.use_int8:
          dense = network.add_convolution_nd(input_tensor, 2, (1, 1), W_out, B_out)
      else:
          dense = network.add_fully_connected(input_tensor, 2, W_out, B_out)
  
      OUT = network.add_shuffle(dense.get_output(0))
      if config.use_int8 and config.interleaved:
          OUT.second_transpose = (1, 2, 0, 3)
      else:
          OUT.second_transpose = (1, 0, 2, 3)
      set_output_name(OUT, prefix, "squad_logits")
      return OUT
function_tokens: def squad_output ( prefix , config , init_dict , network , input_tensor ) : hidden_size = config . hidden_size W_out = init_dict [ prefix + SQD_W ] B_out = init_dict [ prefix + SQD_B ] if config . use_int8 : dense = network . add_convolution_nd ( input_tensor , 2 , ( 1 , 1 ) , W_out , B_out ) else : dense = network . add_fully_connected ( input_tensor , 2 , W_out , B_out ) OUT = network . add_shuffle ( dense . get_output ( 0 ) ) if config . use_int8 and config . interleaved : OUT . second_transpose = ( 1 , 2 , 0 , 3 ) else : OUT . second_transpose = ( 1 , 0 , 2 , 3 ) set_output_name ( OUT , prefix , "squad_logits" ) return OUT