class_docstr: Runtime metrics for a PhysicalOperator.
  
  Metrics are updated dynamically during the execution of the Dataset.
  This class can be used for either observablity or scheduling purposes.
  
  DO NOT modify the fields of this class directly. Instead, use the provided
  callback methods.
class_docstr_tok: ["Runtime","metrics","for","a","PhysicalOperator",".","Metrics","are","updated","dynamically","during","the","execution","of","the","Dataset",".","This","class","can","be","used","for","either","observablity","or","scheduling","purposes",".","DO","NOT","modify","the","fields","of","this","class","directly",".","Instead",",","use","the","provided","callback","methods","."]
code: def __init__(self, op: "PhysicalOperator"):
      from ray.data._internal.execution.operators.map_operator import (
          MapOperator,
      )
  
      self._op = op
      self._is_map = isinstance(op, MapOperator)
      self._running_tasks: Dict[int, RunningTaskInfo] = {}
      self._extra_metrics: Dict[str, Any] = {}
  
imports: from dataclasses import dataclass, field, fields
  from typing import TYPE_CHECKING, Any, Dict, Optional
  import ray
  from ray.data._internal.execution.interfaces.ref_bundle import RefBundle
  from ray.data._internal.memory_tracing import trace_allocation
label_desc: Use image node_id 1 to create a new OpRuntimeMetrics object  with example: obj = OpRuntimeMetrics(op)