class_docstr: Computes cache statistics for uvm_fused_cache tables.
  
  Args:
  
  cachebility (float):
      The area-under-the-curve of miss-ratio curve.
  expected_lookups (float):
      The expected number of unique embedding ids per global batch.
  mrc_hist_counts (torch.Tensor):
      A 1d tensor (size n) holding a histogram of LRU miss ratio curve. Each bin
      represents 1/nth of possible LRU cache sizes (from load_factor 0 to load_factor
      1.0). The bin contains the number of expected LRU operations that could be
      handled without a cache miss if the LRU load_factor was at least that size.
  height (int):
      The height (num_embeddings) of the embedding table.
code: def expected_miss_rate(self, clf: float) -> float:
      cache_size = torch.tensor(clf * self.height)
      miss_rate = EmbeddingOffloadStats.estimate_cache_miss_rate(
          cache_sizes=cache_size, hist=self.hist, bins=self.bins
      )
      return miss_rate.item()
  
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 3 for calling the EmbeddingOffloadStats obj's underlying member method code with example usage: obj.expected_miss_rate(clf) and returns: miss_rate