class_docstr: Modified building SE_TDNN group module block from ECAPA implementation for faster training and inference
  Reference: ECAPA-TDNN Embeddings for Speaker Diarization (https://arxiv.org/pdf/2104.01466.pdf)
  inputs:
      inp_filters: input filter channel size 
      out_filters: output filter channel size
      group_scale: scale value to group wider conv channels (deafult:8)
      se_channels: squeeze and excite output channel size (deafult: 1024/8= 128)
      kernel_size: kernel_size for group conv1d layers (default: 1)
      dilation: dilation size for group conv1d layers  (default: 1)
class_docstr_tok: ["Modified","building","SE_TDNN","group","module","block","from","ECAPA","implementation","for","faster","training","and","inference","Reference",":","ECAPA-TDNN","Embeddings","for","Speaker","Diarization","(","https",":","\/\/arxiv.org\/pdf\/2104.01466.pdf",")","inputs",":","inp_filters",":","input","filter","channel","size","out_filters",":","output","filter","channel","size","group_scale",":","scale","value","to","group","wider","conv","channels","(","deafult:8",")","se_channels",":","squeeze","and","excite","output","channel","size","(","deafult",":","1024\/8=","128",")","kernel_size",":","kernel_size","for","group","conv1d","layers","(","default",":","1",")","dilation",":","dilation","size","for","group","conv1d","layers","(","default",":","1",")"]
code: def forward(self, input, length=None):
      x = self.group_tdnn_block(input)
      x = self.se_layer(x, length)
      return x + input
  
imports: from typing import List
  import torch
  from numpy import inf
  from torch import nn
  from torch.nn import functional
  from nemo.collections.asr.parts.submodules.jasper import get_same_padding, init_weights
label_desc: Use image node_id 2 for calling the TDNNSEModule obj's underlying member method code with example usage: obj.forward(input, length) and returns: unknown