docstr: Creates the embeddings using gensim's Word2Vec.
  :param skip_gram_params: Parameters for gensim.models.Word2Vec - do not supply 'size' / 'vector_size' it is
      taken from the Node2Vec 'dimensions' parameter
  :type skip_gram_params: dict
  :return: A gensim word2vec model
docstr_tok: ["Creates","the","embeddings","using","gensim","'s","Word2Vec",".",":","param","skip_gram_params",":","Parameters","for","gensim.models.Word2Vec","-","do","not","supply","'size","'","\/","'vector_size","'","it","is","taken","from","the","Node2Vec","'dimensions","'","parameter",":","type","skip_gram_params",":","dict",":","return",":","A","gensim","word2vec","model"]
code: def fit(self, **skip_gram_params) -> gensim.models.Word2Vec:
      """
      Creates the embeddings using gensim's Word2Vec.
      :param skip_gram_params: Parameters for gensim.models.Word2Vec - do not supply 'size' / 'vector_size' it is
          taken from the Node2Vec 'dimensions' parameter
      :type skip_gram_params: dict
      :return: A gensim word2vec model
      """
  
      if "workers" not in skip_gram_params:
          skip_gram_params["workers"] = self.workers
  
      # Figure out gensim version, naming of output dimensions changed from size to vector_size in v4.0.0
      gensim_version = pkg_resources.get_distribution("gensim").version
      size = "size" if gensim_version < "4.0.0" else "vector_size"
      if size not in skip_gram_params:
          skip_gram_params[size] = self.dimensions
  
      if "sg" not in skip_gram_params:
          skip_gram_params["sg"] = 1
  
      return gensim.models.Word2Vec(self.walks, **skip_gram_params)
  
file_path: node2vec/node2vec/node2vec.py
imports: import os
  import random
  from collections import defaultdict
  import gensim
  import networkx
  import numpy
  import pkg_resources
  from joblib import Parallel, delayed
  from tqdm.auto import tqdm
  from .parallel import parallel_generate_walks
label_desc: Use image node_id 4 for calling the Node2Vec obj's underlying member method code with example usage: obj.fit() and returns: gensim