class_docstr: Attention pooling layer for pooling speaker embeddings
  Reference: ECAPA-TDNN Embeddings for Speaker Diarization (https://arxiv.org/pdf/2104.01466.pdf)
  inputs:
      inp_filters: input feature channel length from encoder
      attention_channels: intermediate attention channel size
      kernel_size: kernel_size for TDNN and attention conv1d layers (default: 1)
      dilation: dilation size for TDNN and attention conv1d layers  (default: 1) 
class_docstr_tok: ["Attention","pooling","layer","for","pooling","speaker","embeddings","Reference",":","ECAPA-TDNN","Embeddings","for","Speaker","Diarization","(","https",":","\/\/arxiv.org\/pdf\/2104.01466.pdf",")","inputs",":","inp_filters",":","input","feature","channel","length","from","encoder","attention_channels",":","intermediate","attention","channel","size","kernel_size",":","kernel_size","for","TDNN","and","attention","conv1d","layers","(","default",":","1",")","dilation",":","dilation","size","for","TDNN","and","attention","conv1d","layers","(","default",":","1",")"]
code: def __init__(
      self,
      inp_filters: int,
      attention_channels: int = 128,
      kernel_size: int = 1,
      dilation: int = 1,
      eps: float = 1e-10,
  ):
      super().__init__()
  
      self.feat_in = 2 * inp_filters
  
      self.attention_layer = nn.Sequential(
          TDNNModule(
              inp_filters * 3,
              attention_channels,
              kernel_size=kernel_size,
              dilation=dilation,
          ),
          nn.Tanh(),
          nn.Conv1d(
              in_channels=attention_channels,
              out_channels=inp_filters,
              kernel_size=kernel_size,
              dilation=dilation,
          ),
      )
      self.eps = eps
  
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 AttentivePoolLayer object from inherited base classes: nn with example: obj = AttentivePoolLayer(inp_filters, attention_channels, kernel_size, dilation, eps)