class_docstr: A simple default backend that stores the chain in memory
class_docstr_tok: ["A","simple","default","backend","that","stores","the","chain","in","memory"]
docstr: Expand the storage space by some number of samples
  
  Args:
      ngrow (int): The number of steps to grow the chain.
      blobs: The current array of blobs. This is used to compute the
          dtype for the blobs array.
docstr_tok: ["Expand","the","storage","space","by","some","number","of","samples","Args",":","ngrow","(","int",")",":","The","number","of","steps","to","grow","the","chain",".","blobs",":","The","current","array","of","blobs",".","This","is","used","to","compute","the","dtype","for","the","blobs","array","."]
code: def grow(self, ngrow, blobs):
      """Expand the storage space by some number of samples
  
      Args:
          ngrow (int): The number of steps to grow the chain.
          blobs: The current array of blobs. This is used to compute the
              dtype for the blobs array.
  
      """
      self._check_blobs(blobs)
      i = ngrow - (len(self.chain) - self.iteration)
      a = np.empty((i, self.nwalkers, self.ndim), dtype=self.dtype)
      self.chain = np.concatenate((self.chain, a), axis=0)
      a = np.empty((i, self.nwalkers), dtype=self.dtype)
      self.log_prob = np.concatenate((self.log_prob, a), axis=0)
      if blobs is not None:
          dt = np.dtype((blobs.dtype, blobs.shape[1:]))
          a = np.empty((i, self.nwalkers), dtype=dt)
          if self.blobs is None:
              self.blobs = a
          else:
              self.blobs = np.concatenate((self.blobs, a), axis=0)
  
imports: import numpy
  from ..None import autocorr
  from ..state import State
label_desc: Use image node_id 12 for calling the Backend obj's underlying member method code with example usage: obj.grow(ngrow, blobs) without return types