class_docstr: Simple SequenceModule for testing purposes.
class_docstr_tok: ["Simple","SequenceModule","for","testing","purposes","."]
docstr: Initialize SequenceModule.
  
  d_model: input dimension (sometimes denoted H for hidden dimension)
  transposed: if True, inputs have axis ordering (B, H, L) instead of (B, H, L)
docstr_tok: ["Initialize","SequenceModule",".","d_model",":","input","dimension","(","sometimes","denoted","H","for","hidden","dimension",")","transposed",":","if","True",",","inputs","have","axis","ordering","(","B",",","H",",","L",")","instead","of","(","B",",","H",",","L",")"]
code: def __init__(self, d_model, dropout=0.0, **kwargs):
      """Initialize SequenceModule.
  
      d_model: input dimension (sometimes denoted H for hidden dimension)
      transposed: if True, inputs have axis ordering (B, H, L) instead of (B, H, L)
      """
      super().__init__()
      self.d_model = d_model
      self.d_output = d_model
  
file_path: espnet/espnet2/asr/state_spaces/base.py
imports: import functools
  from torch import nn
label_desc: Use image node_id 1 to create a new SequenceIdentity object from inherited base classes: SequenceModule with example: obj = SequenceIdentity(d_model, dropout)