class_docstr: Adds one bias element to the end of the sequence.
  so if the input has a shape ``(L, N, E)``, (``batch_first = False``),
  where ``L`` is the sequence length, ``N`` is the batch size, and ``E`` is
  the embedding dimension, the output will have a shape
  ``(L+1, N, E)``. When ``batch_first = True``, input has a shape ``(N, L, E)``
  and the output will have a shape ``(N, L+1, E)``
  
  Attributes:
      bias (:class:`torch.nn.parameter.Parameter`): the learnable bias of
          the module of shape ``(E)``, where ``E`` is the embedding dimension.
  
  Example:
      >>> m = SequenceBias(16, batch_first=False)
      >>> input = torch.randn(20, 4, 16)
      >>> output = m(input)
      >>> output.size()
      torch.Size([21, 4, 16])
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docstr: assigns Normally distributed random values to bias.
docstr_tok: ["assigns","Normally","distributed","random","values","to","bias","."]
code: def _reset_parameters(self):
      r"""
      assigns Normally distributed random values to bias.
      """
      nn.init.normal_(self.bias)
  
file_path: opacus/opacus/layers/dp_multihead_attention.py
imports: import warnings
  from collections import OrderedDict
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.parameter import Parameter
label_desc: Use image node_id 2 for calling the SequenceBias obj's underlying member method code with example usage: obj._reset_parameters() without return types