docstr: Step one time step as a recurrent model.
  
  Intended to be used during validation.
  
  u: (B H)
  state: (B H N)
  Returns: output (B H), state (B H N)
docstr_tok: ["Step","one","time","step","as","a","recurrent","model",".","Intended","to","be","used","during","validation",".","u",":","(","B","H",")","state",":","(","B","H","N",")","Returns",":","output","(","B","H",")",",","state","(","B","H","N",")"]
code: def step(self, u, state, **kwargs):
      """Step one time step as a recurrent model.
  
      Intended to be used during validation.
  
      u: (B H)
      state: (B H N)
      Returns: output (B H), state (B H N)
      """
      assert not self.training
      y, next_state = self.kernel.step(u, state)  # (B C H)
      y = y + u.unsqueeze(-2) * self.D
      y = rearrange(y, "b c h -> b (c h)")
      y = self.activation(y)
      if self.transposed:
          y = self.output_linear(y.unsqueeze(-1)).squeeze(-1)
      else:
          y = self.output_linear(y)
      return y, 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 4 for calling the S4 obj's underlying member method code with example usage: obj.step(u, state) and returns: y,  next_state