class_docstr: This class implements the RecMetricComputation for MAE, i.e. Mean Absolute Error.
  
  The constructor arguments are defined in RecMetricComputation.
  See the docstring of RecMetricComputation for more detail.
code: def _compute(self) -> List[MetricComputationReport]:
      return [
          MetricComputationReport(
              name=MetricName.MAE,
              metric_prefix=MetricPrefix.LIFETIME,
              value=compute_mae(
                  cast(torch.Tensor, self.error_sum),
                  cast(torch.Tensor, self.weighted_num_samples),
              ),
          ),
          MetricComputationReport(
              name=MetricName.MAE,
              metric_prefix=MetricPrefix.WINDOW,
              value=compute_mae(
                  self.get_window_state(ERROR_SUM),
                  self.get_window_state(WEIGHTED_NUM_SAMPES),
              ),
          ),
      ]
  
file_path: torchrec/torchrec/metrics/mae.py
imports: from typing import Any, cast, Dict, List, Optional, Type
  import torch
  from torchrec.metrics.metrics_namespace import MetricName, MetricNamespace, MetricPrefix
  from torchrec.metrics.rec_metric import MetricComputationReport, RecMetric, RecMetricComputation, RecMetricException
label_desc: Use image node_id 3 for calling the MAEMetricComputation obj's underlying member method code with example usage: obj._compute() and returns: list