original_docstring: 
      Using the model and the given test sets, makes predictions based on the window
      used in the train set and joins the predictions together to make one continuous
      prediction. This multi prediction version is used for models that have multiple
      output predictions.
      
docstring: Using the model and the given test sets, makes predictions based on the window
  used in the train set and joins the predictions together to make one continuous
  prediction. This multi prediction version is used for models that have multiple
  output predictions.
docstring_tokens: Using the model and the given test sets makes predictions based on the window used in the train set and joins the predictions together to make one continuous prediction . This multi prediction version is used for models that have multiple output predictions .
code: def make_bywindow_multi_predictions(model, test_x, test_sets):
      """
      Using the model and the given test sets, makes predictions based on the window
      used in the train set and joins the predictions together to make one continuous
      prediction. This multi prediction version is used for models that have multiple
      output predictions.
      """
      if isinstance(test_x, list):
          lookback = test_x[0].shape[1]
          test_set = [test_x[0][0::lookback], test_x[1][0::lookback]]
      else:
          lookback = test_x.shape[1]
          test_set = test_x[0::lookback]
  
      preds = model.predict(test_set)
  
      preds = [x.flatten() for x in preds]
      truth_sets = [x[0::lookback].flatten() for x in test_sets]
  
      return preds, truth_sets
code_tokens: def make_bywindow_multi_predictions ( model , test_x , test_sets ) : if isinstance ( test_x , list ) : lookback = test_x [ 0 ] . shape [ 1 ] test_set = [ test_x [ 0 ] [ 0 : : lookback ] , test_x [ 1 ] [ 0 : : lookback ] ] else : lookback = test_x . shape [ 1 ] test_set = test_x [ 0 : : lookback ] preds = model . predict ( test_set ) preds = [ x . flatten ( ) for x in preds ] truth_sets = [ x [ 0 : : lookback ] . flatten ( ) for x in test_sets ] return preds , truth_sets
short_docstring: Using the model and the given test sets, makes predictions based on the window
  used in the train set and joins the predictions together to make one continuous
  prediction.
short_docstring_tokens: Using the model and the given test sets makes predictions based on the window used in the train set and joins the predictions together to make one continuous prediction .
comment: """
      Using the model and the given test sets, makes predictions based on the window
      used in the train set and joins the predictions together to make one continuous
      prediction. This multi prediction version is used for models that have multiple
      output predictions.
      """