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: Save a step to the backend
  
  Args:
      state (State): The :class:`State` of the ensemble.
      accepted (ndarray): An array of boolean flags indicating whether
          or not the proposal for each walker was accepted.
docstr_tok: ["Save","a","step","to","the","backend","Args",":","state","(","State",")",":","The",":","class",":","`","State","`","of","the","ensemble",".","accepted","(","ndarray",")",":","An","array","of","boolean","flags","indicating","whether","or","not","the","proposal","for","each","walker","was","accepted","."]
code: def save_step(self, state, accepted):
      """Save a step to the backend
  
      Args:
          state (State): The :class:`State` of the ensemble.
          accepted (ndarray): An array of boolean flags indicating whether
              or not the proposal for each walker was accepted.
  
      """
      self._check(state, accepted)
  
      self.chain[self.iteration, :, :] = state.coords
      self.log_prob[self.iteration, :] = state.log_prob
      if state.blobs is not None:
          self.blobs[self.iteration, :] = state.blobs
      self.accepted += accepted
      self.random_state = state.random_state
      self.iteration += 1
  
imports: import numpy
  from ..None import autocorr
  from ..state import State
label_desc: Use image node_id 14 for calling the Backend obj's underlying member method code with example usage: obj.save_step(state, accepted) without return types