code: def generate_metric_module(
      metric_class: Type[RecMetricModule],
      metrics_config: MetricsConfig,
      batch_size: int,
      world_size: int,
      my_rank: int,
      state_metrics_mapping: Dict[StateMetricEnum, StateMetric],
      device: torch.device,
      process_group: Optional[dist.ProcessGroup] = None,
  ) -> RecMetricModule:
      rec_metrics = _generate_rec_metrics(
          metrics_config, world_size, my_rank, batch_size, process_group
      )
      if metrics_config.throughput_metric:
          throughput_metric = ThroughputMetric(
              batch_size=batch_size,
              world_size=world_size,
              window_seconds=metrics_config.throughput_metric.window_size,
          )
      else:
          throughput_metric = None
      state_metrics = _generate_state_metrics(
          metrics_config, state_metrics_mapping
      )
      metrics = metric_class(
          batch_size=batch_size,
          world_size=world_size,
          rec_tasks=metrics_config.rec_tasks,
          rec_metrics=rec_metrics,
          throughput_metric=throughput_metric,
          state_metrics=state_metrics,
          compute_interval_steps=metrics_config.compute_interval_steps,
          min_compute_interval=metrics_config.min_compute_interval,
          max_compute_interval=metrics_config.max_compute_interval,
      )
      metrics.to(device)
      return 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 3 for calling a global function with example usage: generate_metric_module(metric_class, metrics_config, batch_size, world_size, my_rank, state_metrics_mapping, device, process_group) and returns: metrics