code: def __init__(
      self,
      world_size: int,
      my_rank: int,
      batch_size: int,
      tasks: List[RecTaskInfo],
      compute_mode: RecComputeMode = RecComputeMode.UNFUSED_TASKS_COMPUTATION,
      window_size: int = 100,
      fused_update_limit: int = 0,
      compute_on_all_ranks: bool = False,
      should_validate_update: bool = False,
      process_group: Optional[dist.ProcessGroup] = None,
      **kwargs: Dict[str, Any],
  ) -> None:
      super().__init__(
          world_size=world_size,
          my_rank=my_rank,
          batch_size=batch_size,
          tasks=tasks,
          compute_mode=compute_mode,
          window_size=window_size,
          fused_update_limit=fused_update_limit,
          compute_on_all_ranks=compute_on_all_ranks,
          should_validate_update=should_validate_update,
          process_group=process_group,
          **kwargs,
      )
      self._required_inputs.add("grouping_keys")
  
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 1 to create a new SegmentedNEMetric object from inherited base classes: RecMetric with example: obj = SegmentedNEMetric(world_size, my_rank, batch_size, tasks, compute_mode, window_size, fused_update_limit, compute_on_all_ranks, should_validate_update, process_group)