class_docstr: Embedding Wall Time Perf Estimator
code: def __init__(
      self,
      topology: Topology,
      constraints: Optional[Dict[str, ParameterConstraints]] = None,
      is_inference: bool = False,
  ) -> None:
      self._topology = topology
      self._constraints = constraints
      self._is_inference = is_inference
  
imports: import logging
  import math
  from typing import cast, Dict, List, Optional, Tuple, Type
  import torch
  import torchrec.optim
  from torch import nn
  from torchrec.distributed.embedding_types import EmbeddingComputeKernel
  from torchrec.distributed.planner.constants import BATCHED_COPY_PERF_FACTOR, BIGINT_DTYPE, DP_ELEMENTWISE_KERNELS_PERF_FACTOR, FULL_BLOCK_EMB_DIM, HALF_BLOCK_PENALTY, kernel_bw_lookup, QUARTER_BLOCK_PENALTY, UVM_CACHING_RATIO, WEIGHTED_KERNEL_MULTIPLIER
  from torchrec.distributed.planner.types import ParameterConstraints, Perf, PlannerError, ShardEstimator, ShardingOption, Storage, Topology
  from torchrec.distributed.planner.utils import prod, sharder_name
  from torchrec.distributed.types import CacheStatistics, CommOp, ModuleSharder, ShardingType
  from torchrec.modules.embedding_modules import EmbeddingBagCollectionInterface
label_desc: Use image node_id 1 to create a new EmbeddingPerfEstimator object from inherited base classes: ShardEstimator with example: obj = EmbeddingPerfEstimator(topology, constraints, is_inference)