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 __init__(
      self,
      inp_filters: int,
      out_filters: int,
      group_scale: int = 8,
      se_channels: int = 128,
      kernel_size: int = 1,
      dilation: int = 1,
      init_mode: str = "xavier_uniform",
  ):
      super().__init__()
      self.out_filters = out_filters
      padding_val = get_same_padding(
          kernel_size=kernel_size, dilation=dilation, stride=1
      )
  
      group_conv = nn.Conv1d(
          out_filters,
          out_filters,
          kernel_size=kernel_size,
          dilation=dilation,
          padding=padding_val,
          groups=group_scale,
      )
      self.group_tdnn_block = nn.Sequential(
          TDNNModule(
              inp_filters, out_filters, kernel_size=1, dilation=1
          ),
          group_conv,
          nn.ReLU(),
          nn.BatchNorm1d(out_filters),
          TDNNModule(
              out_filters, out_filters, kernel_size=1, dilation=1
          ),
      )
  
      self.se_layer = MaskedSEModule(
          out_filters, se_channels, out_filters
      )
  
      self.apply(lambda x: init_weights(x, mode=init_mode))
  
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 1 to create a new TDNNSEModule object from inherited base classes: nn with example: obj = TDNNSEModule(inp_filters, out_filters, group_scale, se_channels, kernel_size, dilation, init_mode)