class_docstr: Stores a representation of and computes the SSKernel function.
  
  K_L(A^dt, B^dt, C) corresponding to a discretized state space,
  where A is Normal + Low Rank (NPLR)
class_docstr_tok: ["Stores","a","representation","of","and","computes","the","SSKernel","function",".","K_L","(","A^dt",",","B^dt",",","C",")","corresponding","to","a","discretized","state","space",",","where","A","is","Normal","+","Low","Rank","(","NPLR",")"]
docstr: Step one time step as a recurrent model.
  
  Must be called after self.default_state() is used to construct an initial state!
docstr_tok: ["Step","one","time","step","as","a","recurrent","model",".","Must","be","called","after","self.default_state","(",")","is","used","to","construct","an","initial","state","!"]
code: def _step_state(self, u, state):
      """Step one time step as a recurrent model.
  
      Must be called after self.default_state() is used to construct an initial state!
      """
      next_state = self.state_contraction(
          self.dA, state
      ) + self.input_contraction(self.dB, u)
      return next_state
  
imports: import logging
  import math
  import os
  from functools import wraps
  from typing import Any, Callable, Optional
  import numpy
  import opt_einsum
  import torch
  import torch.nn
  import torch.nn.functional
  from einops import rearrange, repeat
  from espnet2.asr.state_spaces.components import Activation, DropoutNd, LinearActivation
label_desc: Use image node_id 10 for calling the SSKernelNPLR obj's underlying member method code with example usage: obj._step_state(u, state) and returns: next_state