from gensim.models import Word2Vec # Load a pre-trained Word2Vec model from disk model = Word2Vec.load('word2vec_model.bin') # Get the vector representation of a word vector = model.wv['dog'] # Find similar words to a given word similar_words = model.wv.most_similar('dog') # Perform vector arithmetic on words result = model.wv.most_similar(positive=['king', 'woman'], negative=['man'])In the first line of code, we import the `Word2Vec` class from the `gensim.models` module. We then load an existing Word2Vec model from a file called `word2vec_model.bin`. Next, we use the loaded model to get the vector representation of a specific word (`dog`) using the `model.wv` attribute. We can also find words that are similar to our target word using the `most_similar()` method of the `model.wv` attribute. Additionally, we can perform vector arithmetic on words by providing positive and negative input words to the `most_similar()` method. Overall, these code examples illustrate how you can use the `load()` function in the `gensim.models.Word2Vec` class to work with existing Word2Vec models in Python.