code: def _compute_cross_entropy_norm(
      mean_label: torch.Tensor,
      pos_labels: torch.Tensor,
      neg_labels: torch.Tensor,
      eta: float,
  ) -> torch.Tensor:
      mean_label = mean_label.double()
      mean_label.clamp_(min=eta, max=1 - eta)
      return -pos_labels * torch.log2(
          mean_label
      ) - neg_labels * torch.log2(1.0 - mean_label)
  
file_path: torchrec/torchrec/metrics/ne.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 2 for calling a global function with example usage: _compute_cross_entropy_norm(mean_label, pos_labels, neg_labels, eta) and returns: unknown