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 get_sampling_frames(self):
      return self.subsampling_factor
  
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 2 for calling the StackingSubsampling obj's underlying member method code with example usage: obj.get_sampling_frames() and returns: self