docstr: Load a Sense2Vec object from a bytestring.
  
  bytes_data (bytes): The data to load.
  exclude (list): Names of serialization fields to exclude.
  RETURNS (Sense2Vec): The loaded object.
docstr_tok: ["Load","a","Sense2Vec","object","from","a","bytestring",".","bytes_data","(","bytes",")",":","The","data","to","load",".","exclude","(","list",")",":","Names","of","serialization","fields","to","exclude",".","RETURNS","(","Sense2Vec",")",":","The","loaded","object","."]
code: def from_bytes(
      self, bytes_data: bytes, exclude: Sequence[str] = tuple()
  ):
      """Load a Sense2Vec object from a bytestring.
  
      bytes_data (bytes): The data to load.
      exclude (list): Names of serialization fields to exclude.
      RETURNS (Sense2Vec): The loaded object.
      """
      data = srsly.msgpack_loads(bytes_data)
      self.vectors = Vectors().from_bytes(data["vectors"])
      # Pin vectors to the CPU so that we don't end up comparing
      # numpy and cupy arrays.
      self.vectors.to_ops(NumpyOps())
      self.freqs = dict(data.get("freqs", []))
      self.cfg.update(data.get("cfg", {}))
      if "strings" not in exclude and "strings" in data:
          self.strings = StringStore().from_bytes(data["strings"])
      if "cache" not in exclude and "cache" in data:
          self.cache = data.get("cache", {})
      self._row2key = None
      return self
  
imports: from typing import Tuple, List, Union, Sequence, Dict, Callable, Any
  from pathlib import Path
  from spacy.vectors import Vectors
  from spacy.strings import StringStore
  from spacy.util import SimpleFrozenDict
  from thinc.api import NumpyOps
  import numpy
  import srsly
  from .util import registry, cosine_similarity
label_desc: Use image node_id 24 for calling the Sense2Vec obj's underlying member method code with example usage: obj.from_bytes(bytes_data, exclude) and returns: self