code: def _generate_rec_metrics(
      metrics_config: MetricsConfig,
      world_size: int,
      my_rank: int,
      batch_size: int,
      process_group: Optional[dist.ProcessGroup] = None,
  ) -> RecMetricList:
      rec_metrics = []
      for metric_enum, metric_def in metrics_config.rec_metrics.items():
          kwargs: Dict[str, Any] = {}
          if metric_def and metric_def.arguments is not None:
              kwargs = metric_def.arguments
  
          rec_tasks: List[RecTaskInfo] = []
          if metric_def.rec_tasks and metric_def.rec_task_indices:
              raise ValueError(
                  "Only one of RecMetricDef.rec_tasks and RecMetricDef.rec_task_indices "
                  "should be specified."
              )
          if metric_def.rec_tasks:
              rec_tasks = metric_def.rec_tasks
          elif metric_def.rec_task_indices:
              rec_tasks = [
                  metrics_config.rec_tasks[idx]
                  for idx in metric_def.rec_task_indices
              ]
          else:
              raise ValueError(
                  "One of RecMetricDef.rec_tasks and RecMetricDef.rec_task_indices "
                  "should be a non-empty list"
              )
  
          rec_metrics.append(
              REC_METRICS_MAPPING[metric_enum](
                  world_size=world_size,
                  my_rank=my_rank,
                  batch_size=batch_size,
                  tasks=rec_tasks,
                  compute_mode=metrics_config.rec_compute_mode,
                  window_size=metric_def.window_size,
                  fused_update_limit=metrics_config.fused_update_limit,
                  compute_on_all_ranks=metrics_config.compute_on_all_ranks,
                  should_validate_update=metrics_config.should_validate_update,
                  process_group=process_group,
                  **kwargs,
              )
          )
      return RecMetricList(rec_metrics)
  
imports: import abc
  import logging
  import time
  from typing import Any, Dict, List, Optional, Type, Union
  import torch
  import torch.distributed
  import torch.nn
  from torch.profiler import record_function
  from torchrec.metrics.accuracy import AccuracyMetric
  from torchrec.metrics.auc import AUCMetric
  from torchrec.metrics.auprc import AUPRCMetric
  from torchrec.metrics.calibration import CalibrationMetric
  from torchrec.metrics.ctr import CTRMetric
  from torchrec.metrics.mae import MAEMetric
  from torchrec.metrics.metrics_config import MetricsConfig, RecMetricEnum, RecMetricEnumBase, RecTaskInfo, StateMetricEnum
  from torchrec.metrics.metrics_namespace import compose_customized_metric_key, compose_metric_namespace, MetricNamespace
  from torchrec.metrics.model_utils import parse_task_model_outputs
  from torchrec.metrics.mse import MSEMetric
  from torchrec.metrics.multiclass_recall import MulticlassRecallMetric
  from torchrec.metrics.ndcg import NDCGMetric
  from torchrec.metrics.ne import NEMetric
  from torchrec.metrics.rec_metric import RecMetric, RecMetricList
  from torchrec.metrics.recall_session import RecallSessionMetric
  from torchrec.metrics.segmented_ne import SegmentedNEMetric
  from torchrec.metrics.throughput import ThroughputMetric
  from torchrec.metrics.tower_qps import TowerQPSMetric
  from torchrec.metrics.weighted_avg import WeightedAvgMetric
label_desc: Use image node_id 1 for calling a global function with example usage: _generate_rec_metrics(metrics_config, world_size, my_rank, batch_size, process_group) and returns: RecMetricList