code: def compute_mae(
      error_sum: torch.Tensor, weighted_num_samples: torch.Tensor
  ) -> torch.Tensor:
      return torch.where(
          weighted_num_samples == 0.0,
          0.0,
          error_sum / weighted_num_samples,
      ).double()
  
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 1 for calling a global function with example usage: compute_mae(error_sum, weighted_num_samples) and returns: torch