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
  :param x_dim: int, default 0, dimension of transformation matrix for x-axis
  :param y_dim: int, default 1, dimension of transformation matrix for y-axis
  :return:
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code: def project_embeddings(self, projection_model=None, x_dim=0, y_dim=1):
      """
  
      :param projection_model: sklearn unsupervised model (e.g., PCA) by default the recommended model is umap.UMAP,
          which requires UMAP in to be installed
      :param x_dim: int, default 0, dimension of transformation matrix for x-axis
      :param y_dim: int, default 1, dimension of transformation matrix for y-axis
      :return:
      """
      axes = self.project(projection_model)
      word_axes = (
          pd.DataFrame(
              {
                  "term": [w for w in self.vocab_],
                  "x": axes.T[x_dim],
                  "y": axes.T[y_dim],
              }
          )
          .set_index("term")
          .reindex(pd.Series(self.corpus_.get_terms()))
          .dropna()
      )
      self.corpus_ = self.corpus_.remove_terms(
          set(self.corpus_.get_terms()) - set(word_axes.index)
      )
      word_axes = word_axes.reindex(self.corpus_.get_terms()).dropna()
  
      return self.corpus_, word_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 4 for calling the EmbeddingsResolver obj's underlying member method code with example usage: obj.project_embeddings(projection_model, x_dim, y_dim) and returns: self,  word_axes