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",")"]
code: def _w(self):
      # Get the internal w (diagonal) parameter
      if self.real_type == "none":
          w_real = -self.inv_w_real
      elif self.real_type == "exp":
          w_real = -torch.exp(self.inv_w_real)
      elif self.real_type == "relu":
          w_real = -F.relu(self.inv_w_real)
      elif self.real_type == "sigmoid":
          w_real = -F.sigmoid(self.inv_w_real)
      elif self.real_type == "softplus":
          w_real = -F.softplus(self.inv_w_real)
      else:
          raise NotImplementedError
      w = w_real + 1j * self.w_imag
      return w
  
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 5 for calling the SSKernelNPLR obj's underlying member method code with example usage: obj._w() and returns: w