input: Write a python function to convert a GloVe file to Word2Vec format and use it to train a logistic regression model on text review data.
  assert isinstance(convert_glove_to_word2vec_and_train(["good movie", "bad film"], [1, 0], "glove_path"), float)
  assert convert_glove_to_word2vec_and_train(["excellent", "terrible"], [1, 0], "glove_path") > 0.5
  assert convert_glove_to_word2vec_and_train(["poor acting", "great direction"], [0, 1], "glove_path") < -0.5
output: def convert_glove_to_word2vec_and_train(reviews, labels, glove_path):
      import gensim
      from gensim.models import KeyedVectors
      from sklearn.linear_model import LogisticRegression
      from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.pipeline import make_pipeline
      from sklearn.preprocessing import StandardScaler
      import numpy as np
  
      # Convert GloVe to Word2Vec format
      glove_w2v_file = "glove_w2v.txt"
      glove2word2vec(glove_path, glove_w2v_file)
      model = KeyedVectors.load_word2vec_format(glove_w2v_file, binary=False)
  
      # Create a vectorizer using the loaded Word2Vec model
      class MeanEmbeddingVectorizer:
          def __init__(self, word2vec):
              self.word2vec = word2vec
              self.dim = len(next(iter(word2vec.values())))
  
          def fit(self, X, y):
              return self
  
          def transform(self, X):
              return np.array([
                  np.mean([self.word2vec[w] for w in words if w in self.word2vec]
                          or [np.zeros(self.dim)], axis=0)
                  for words in X
              ])
  
      # Preprocess the reviews to get word tokens
      def preprocess(text):
          return text.lower().split()
  
      reviews = [preprocess(review) for review in reviews]
  
      # Create a pipeline with a vectorizer and logistic regression
      model_pipeline = make_pipeline(
          MeanEmbeddingVectorizer(model),
          StandardScaler(),
          LogisticRegression()
      )
  
      # Train the model
      model_pipeline.fit(reviews, labels)
  
      # Make predictions and return the logistic regression intercept
      predictions = model_pipeline.predict(reviews)
      return float(model_pipeline.named_steps['logisticregression'].intercept_[0])