docstr: :param projection_model: sklearn unsupervised model (e.g., PCA) by default the recommended model is umap.UMAP,
  which requires UMAP in to be installed
  
  :return: array, shape (num dimension, vocab size)
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code: def project(self, projection_model=None):
      """
      :param projection_model: sklearn unsupervised model (e.g., PCA) by default the recommended model is umap.UMAP,
      which requires UMAP in to be installed
  
      :return: array, shape (num dimension, vocab size)
      """
      if self.embeddings_ is None:
          raise Exception(
              "Run set_embeddings_model or set_embeddings to get embeddings"
          )
      if projection_model is None:
          try:
              import umap
          except:
              raise Exception(
                  "Please install umap (pip install umap-learn) to use the default projection_model."
              )
          projection_model = umap.UMAP(min_dist=0.5, metric="cosine")
      axes = projection_model.fit_transform(self.embeddings_)
      return axes
  
file_path: scattertext/scattertext/representations/EmbeddingsResolver.py
imports: import re
  import numpy
  import pandas
  from scipy import sparse
  from scipy.stats import pearsonr
  from scattertext.representations import Word2VecFromParsedCorpus
label_desc: Use image node_id 5 for calling the EmbeddingsResolver obj's underlying member method code with example usage: obj.project(projection_model) and returns: axes