class_docstr: Simulates the live deployment of an forecaster model.
docstr: :param ground_truth: the series of test data
  :param predict: the series of predicted values
  :param metric: the evaluation metric.
docstr_tok: [":","param","ground_truth",":","the","series","of","test","data",":","param","predict",":","the","series","of","predicted","values",":","param","metric",":","the","evaluation","metric","."]
code: def evaluate(
      self,
      ground_truth: TimeSeries,
      predict: Union[TimeSeries, List[TimeSeries]],
      metric: ForecastMetric = ForecastMetric.sMAPE,
  ):
      """
      :param ground_truth: the series of test data
      :param predict: the series of predicted values
      :param metric: the evaluation metric.
      """
      if self.model.target_seq_index is not None:
          name = ground_truth.names[self.model.target_seq_index]
          ground_truth = ground_truth.univariates[name].to_ts()
      if isinstance(predict, TimeSeries):
          if metric is not None:
              return metric.value(ground_truth, predict)
          return accumulate_forecast_score(ground_truth, predict)
      else:
          if metric is not None:
              weights = np.asarray(
                  [len(p) for p in predict if not p.is_empty()]
              )
              vals = [
                  metric.value(ground_truth, p)
                  for p in predict
                  if not p.is_empty()
              ]
              return np.dot(weights / weights.sum(), vals)
          return [
              accumulate_forecast_score(ground_truth, p)
              for p in predict
              if not p.is_empty()
          ]
  
imports: from enum import Enum
  from functools import partial
  from typing import List, Union, Tuple
  import warnings
  import numpy
  import pandas
  from merlion.evaluate.base import EvaluatorBase, EvaluatorConfig
  from merlion.models.forecast.base import ForecasterBase
  from merlion.utils import TimeSeries, UnivariateTimeSeries
  from merlion.utils.resample import to_offset
label_desc: Use image node_id 5 for calling the ForecastEvaluator obj's underlying member method code with example usage: obj.evaluate(ground_truth, predict, metric) and returns: accumulate_forecast_score, unknown, metric, np