code: def correlation_sums(indicators, max_dim):
      """
      Calculate all correlation sums for embedding dimensions 1:max_dim
  
      Parameters
      ----------
      indicators : 2d array
          matrix of distance threshold indicators
      max_dim : int
          maximum embedding dimension
  
      Returns
      -------
      corrsums : ndarray
          Correlation sums
      """
  
      corrsums = np.zeros((1, max_dim))
  
      corrsums[0, 0], indicators = correlation_sum(indicators, 1)
      for i in range(1, max_dim):
          corrsums[0, i], indicators = correlation_sum(indicators, 2)
  
      return corrsums
  
file_path: statsmodels/statsmodels/tsa/_bds.py
imports: import numpy
  from scipy import stats
  from statsmodels.tools.validation import array_like
label_desc: Use image node_id 3 for calling a global function with example usage: correlation_sums(indicators, max_dim) and returns: corrsums