code: def evaluate(data_source, batch_size, params_file_name, ctx=None):
      
  
      total_L = 0.0
      ntotal = 0
  
      model_eval.load_parameters(params_file_name, context)
  
      hidden = model_eval.begin_state(batch_size=batch_size, func=mx.nd.zeros, ctx=context[0])
      i = 0
      while i < len(data_source) - 1 - 1:
          data, target = get_batch(data_source, i, seq_len=args.bptt)
          data = data.as_in_context(ctx)
          target = target.as_in_context(ctx)
          output, hidden = model_eval(data, hidden)
          hidden = detach(hidden)
          L = loss(output.reshape(-3, -1),
                   target.reshape(-1,))
          total_L += mx.nd.sum(L).asscalar()
          ntotal += L.size
          i += args.bptt
      return total_L / ntotal
docs: Evaluate the model on the dataset.
  
      Parameters
      ----------
      data_source : NDArray
          The dataset is evaluated on.
      batch_size : int
          The size of the mini-batch.
      params_file_name : str
          The parameter file to use to evaluate,
          e.g., val.params or args.save
      ctx : mx.cpu() or mx.gpu()
          The context of the computation.
  
      Returns
      -------
      loss: float
          The loss on the dataset