class_docstr: Mean squared error.
  
  Using this metric will lead to forecast of the mean.
  
  .. math::
  
      \operatorname{MSE} = \frac{1}{N} \frac{1}{H} \sum_{i=1}^{N}\sum_{t=T+1}^{T+H}  (y_{i,t} - f_{i,t})^2
  
  Properties:
  
  - scale-dependent (time series with large absolute value contribute more to the loss)
  - heavily penalizes models that cannot quickly adapt to abrupt changes in the time series
  - sensitive to outliers
  - prefers models that accurately estimate the mean (expected value)
  
  References
  ----------
  - `Wikipedia <https://en.wikipedia.org/wiki/Mean_squared_error>`_
code: def compute_metric(
      self,
      data_future: TimeSeriesDataFrame,
      predictions: TimeSeriesDataFrame,
      target: str = "target",
      **kwargs,
  ) -> float:
      y_true, y_pred = self._get_point_forecast_score_inputs(
          data_future, predictions, target=target
      )
      return self._safemean((y_true - y_pred) ** 2)
  
imports: import logging
  from typing import Optional
  import numpy
  import pandas
  from autogluon.timeseries import TimeSeriesDataFrame
  from autogluon.timeseries.dataset.ts_dataframe import ITEMID
  from .abstract import TimeSeriesScorer
  from .utils import _in_sample_abs_seasonal_error, _in_sample_squared_seasonal_error
label_desc: Use image node_id 1 for calling the MSE obj's underlying member method code with example usage: obj.compute_metric(data_future, predictions, target) and returns: self