class_docstr: Linear 1D subsampling.
  
  :param int idim: input dim
  :param int odim: output dim
  :param flaot dropout_rate: dropout rate
class_docstr_tok: ["Linear","1D","subsampling",".",":","param","int","idim",":","input","dim",":","param","int","odim",":","output","dim",":","param","flaot","dropout_rate",":","dropout","rate"]
docstr: Initialize LinearSampling.
docstr_tok: ["Initialize","LinearSampling","."]
code: def __init__(
      self, idim, dims, dropout=0.1, initialW=None, initial_bias=None
  ):
      """Initialize LinearSampling."""
      super(LinearSampling, self).__init__()
      stvd = 1.0 / np.sqrt(dims)
      self.dropout = dropout
      with self.init_scope():
          self.linear = L.Linear(
              idim,
              dims,
              initialW=initialW(scale=stvd),
              initial_bias=initial_bias(scale=stvd),
          )
          self.pe = PositionalEncoding(dims, dropout)
  
file_path: espnet/espnet/nets/chainer_backend/transformer/subsampling.py
imports: import logging
  import chainer
  import chainer.functions
  import chainer.links
  import numpy
  from espnet.nets.chainer_backend.transformer.embedding import PositionalEncoding
label_desc: Use image node_id 1 to create a new LinearSampling object from inherited base classes: chainer with example: obj = LinearSampling(idim, dims, dropout, initialW, initial_bias)