class_docstr: Stats for a sharding planner execution.
class_docstr_tok: ["Stats","for","a","sharding","planner","execution","."]
code: def __init__(self) -> None:
      self._width: int = MIN_WIDTH
      self._stats_table: List[str] = []
  
imports: import logging
  from collections import defaultdict
  from typing import Any, cast, Dict, List, Optional, Tuple, Union
  from torch import nn
  from torchrec.distributed.planner.constants import BIGINT_DTYPE, NUM_POOLINGS
  from torchrec.distributed.planner.shard_estimators import _calculate_shard_io_sizes
  from torchrec.distributed.planner.storage_reservations import FixedPercentageStorageReservation, HeuristicalStorageReservation, InferenceStorageReservation
  from torchrec.distributed.planner.types import ParameterConstraints, Perf, ShardingOption, Stats, Storage, StorageReservation, Topology
  from torchrec.distributed.planner.utils import _find_imbalance_tables, bytes_to_gb, bytes_to_mb, sharder_name
  from torchrec.distributed.types import ModuleSharder, ParameterSharding, ShardingPlan, ShardingType
label_desc: Use image node_id 1 to create a new EmbeddingStats object from inherited base classes: Stats with example: obj = EmbeddingStats()