class_docstr: Isotropic deep sequence model backbone, in the style of ResNets / Transformers.
  
  The SequenceModel class implements a generic
  (batch, length, d_input) -> (batch, length, d_output) transformation
  
  Args:
      d_model: Resize input (useful for deep models with residuals)
      n_layers: Number of layers
      transposed: Transpose inputs so each layer receives (batch, dim, length)
      dropout: Dropout parameter applied on every residual and every layer
      tie_dropout: Tie dropout mask across sequence like nn.Dropout1d/nn.Dropout2d
      prenorm: Pre-norm vs. post-norm
      n_repeat: Each layer is repeated n times per stage before applying pooling
      layer: Layer config, must be specified
      residual: Residual config
      norm: Normalization config (e.g. layer vs batch)
      pool: Config for pooling layer per stage
      track_norms: Log norms of each layer output
      dropinp: Input dropout
      drop_path: Stochastic depth for each residual path
code: def default_state(self, *batch_shape, device=None):
      return [
          layer.default_state(*batch_shape, device=device)
          for layer in self.layers
      ]
  
file_path: espnet/espnet2/asr/state_spaces/model.py
imports: from functools import partial
  import torch
  import torch.nn
  from einops import rearrange
  from espnet2.asr.state_spaces.base import SequenceModule
  from espnet2.asr.state_spaces.block import SequenceResidualBlock
  from espnet2.asr.state_spaces.components import DropoutNd, Normalization
  from espnet2.asr.state_spaces.utils import to_dict, to_list
label_desc: Use image node_id 5 for calling the SequenceModel obj's underlying member method code with example usage: obj.default_state() and returns: unknown