parameters: (config, init_dict, network, input_tensor, input_mask)
return_statement: return prev_input
docstring: Create the bert model
docstring_summary: Create the bert model
docstring_tokens: Create the bert model
function: def bert_model(config, init_dict, network, input_tensor, input_mask):
      """
      Create the bert model
      """
      prev_input = input_tensor
      for layer in range(0, config.num_hidden_layers):
          ss = "l{}_".format(layer)
          out_layer = transformer_layer_opt(ss, config,  init_dict, network, prev_input, input_mask)
          prev_input = out_layer.get_output(0)
  
      if config.use_qat:
          dr_out = init_dict["bert_encoder_final_input_quantizer_amax"]
          set_output_range(out_layer, dr_out)
      return prev_input
function_tokens: def bert_model ( config , init_dict , network , input_tensor , input_mask ) : prev_input = input_tensor for layer in range ( 0 , config . num_hidden_layers ) : ss = "l{}_" . format ( layer ) out_layer = transformer_layer_opt ( ss , config , init_dict , network , prev_input , input_mask ) prev_input = out_layer . get_output ( 0 ) if config . use_qat : dr_out = init_dict [ "bert_encoder_final_input_quantizer_amax" ] set_output_range ( out_layer , dr_out ) return prev_input