code: def compute_ne_helper(
      ce_sum: torch.Tensor,
      weighted_num_samples: torch.Tensor,
      pos_labels: torch.Tensor,
      neg_labels: torch.Tensor,
      eta: float,
  ) -> torch.Tensor:
      mean_label = pos_labels / weighted_num_samples
      ce_norm = _compute_cross_entropy_norm(
          mean_label, pos_labels, neg_labels, eta
      )
      return ce_sum / ce_norm
  
imports: from typing import Any, Dict, List, Optional, Type
  import torch
  from torch import distributed
  from torchrec.metrics.metrics_config import RecComputeMode, RecTaskInfo
  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 a global function with example usage: compute_ne_helper(ce_sum, weighted_num_samples, pos_labels, neg_labels, eta) and returns: unknown