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: Construct dA and dB for discretized state equation.
docstr_tok: ["Construct","dA","and","dB","for","discretized","state","equation","."]
code: def _setup_state(self):
      """Construct dA and dB for discretized state equation."""
      # Construct dA and dB by using the stepping
      self._setup_linear()
      C = _r2c(
          self.C
      )  # Just returns a view that we use for finding dtype/device
  
      state = torch.eye(
          2 * self.N, dtype=C.dtype, device=C.device
      ).unsqueeze(
          -2
      )  # (N 1 N)
      dA = self._step_state_linear(state=state)
      dA = rearrange(dA, "n h m -> h m n")
  
      u = C.new_ones(self.H)
      dB = self._step_state_linear(u=u)
      dB = _conj(dB)
      dB = rearrange(dB, "1 h n -> h n")  # (H N)
      return dA, dB
  
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 9 for calling the SSKernelNPLR obj's underlying member method code with example usage: obj._setup_state() and returns: dA,  dB