docstr: Specifies fixed set of embeddings
  :param embeddings: array-like, sparse or dense, shape should be (embedding size, # terms)
  :return: EmbeddingsResolver
docstr_tok: ["Specifies","fixed","set","of","embeddings",":","param","embeddings",":","array-like",",","sparse","or","dense",",","shape","should","be","(","embedding","size",",","#","terms",")",":","return",":","EmbeddingsResolver"]
code: def set_embeddings(self, embeddings):
      """
      Specifies fixed set of embeddings
      :param embeddings: array-like, sparse or dense, shape should be (embedding size, # terms)
      :return: EmbeddingsResolver
      """
      if self.embeddings_ is not None:
          raise Exception(
              "You have already set embeddings by running set_embeddings or set_embeddings_model."
          )
      assert embeddings.shape[1] == self.corpus_.get_num_terms()
      self.embeddings_ = embeddings.T
      self.vocab_ = self.corpus_.get_terms()
      return self
  
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 2 for calling the EmbeddingsResolver obj's underlying member method code with example usage: obj.set_embeddings(embeddings) and returns: self