class_docstr: Stacking subsampling which simply stacks consecutive frames to reduce the sampling rate
  Args:
      subsampling_factor (int): The subsampling factor
      feat_in (int): size of the input features
      feat_out (int): size of the output features
      norm (bool): whether to use an MLP layer after the stacking along with normalization. default is False.
class_docstr_tok: ["Stacking","subsampling","which","simply","stacks","consecutive","frames","to","reduce","the","sampling","rate","Args",":","subsampling_factor","(","int",")",":","The","subsampling","factor","feat_in","(","int",")",":","size","of","the","input","features","feat_out","(","int",")",":","size","of","the","output","features","norm","(","bool",")",":","whether","to","use","an","MLP","layer","after","the","stacking","along","with","normalization",".","default","is","False","."]
code: def __init__(self, subsampling_factor, feat_in, feat_out, norm=False):
      super(StackingSubsampling, self).__init__()
      self.subsampling_factor = subsampling_factor
      self.proj_out = torch.nn.Linear(
          subsampling_factor * feat_in, feat_out
      )
      if norm:
          self.pre_norm = LayerNorm(feat_in)
      else:
          self.pre_norm = None
  
imports: import math
  import torch
  import torch.nn
  from torch.nn import LayerNorm
  from nemo.collections.asr.parts.submodules.causal_convs import CausalConv1D, CausalConv2D
  from nemo.utils import logging
label_desc: Use image node_id 1 to create a new StackingSubsampling object from inherited base classes: torch.nn with example: obj = StackingSubsampling(subsampling_factor, feat_in, feat_out, norm)