class_docstr: Squeezeformer Time Reduction procedure. Downsamples the audio by `stride` in the time dimension.
  
  Args:
      d_model (int): input dimension of MultiheadAttentionMechanism and PositionwiseFeedForward
      out_dim (int): Output dimension of the module.
      kernel_size (int): Conv kernel size for depthwise convolution in convolution module
      stride (int): Downsampling factor in time dimension.
class_docstr_tok: ["Squeezeformer","Time","Reduction","procedure",".","Downsamples","the","audio","by","`","stride","`","in","the","time","dimension",".","Args",":","d_model","(","int",")",":","input","dimension","of","MultiheadAttentionMechanism","and","PositionwiseFeedForward","out_dim","(","int",")",":","Output","dimension","of","the","module",".","kernel_size","(","int",")",":","Conv","kernel","size","for","depthwise","convolution","in","convolution","module","stride","(","int",")",":","Downsampling","factor","in","time","dimension","."]
code: def reset_parameters(self):
      dw_max = self.kernel_size**-0.5
      pw_max = self.d_model**-0.5
  
      with torch.no_grad():
          torch.nn.init.uniform_(self.dw_conv.weight, -dw_max, dw_max)
          torch.nn.init.uniform_(self.dw_conv.bias, -dw_max, dw_max)
          torch.nn.init.uniform_(self.pw_conv.weight, -pw_max, pw_max)
          torch.nn.init.uniform_(self.pw_conv.bias, -pw_max, pw_max)
  
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 3 for calling the TimeReductionModule obj's underlying member method code with example usage: obj.reset_parameters() without return types