class_docstr: Squeeze and Excite module implementation with conv1d layers
  input:
      inp_filters: input filter channel size 
      se_filters: intermediate squeeze and excite channel output and input size
      out_filters: output filter channel size
      kernel_size: kernel_size for both conv1d layers
      dilation: dilation size for both conv1d layers
  
  output:
      squeeze and excite layer output
class_docstr_tok: ["Squeeze","and","Excite","module","implementation","with","conv1d","layers","input",":","inp_filters",":","input","filter","channel","size","se_filters",":","intermediate","squeeze","and","excite","channel","output","and","input","size","out_filters",":","output","filter","channel","size","kernel_size",":","kernel_size","for","both","conv1d","layers","dilation",":","dilation","size","for","both","conv1d","layers","output",":","squeeze","and","excite","layer","output"]
code: def __init__(
      self,
      inp_filters: int,
      se_filters: int,
      out_filters: int,
      kernel_size: int = 1,
      dilation: int = 1,
  ):
      super().__init__()
      self.se_layer = nn.Sequential(
          nn.Conv1d(
              inp_filters,
              se_filters,
              kernel_size=kernel_size,
              dilation=dilation,
          ),
          nn.ReLU(),
          nn.BatchNorm1d(se_filters),
          nn.Conv1d(
              se_filters,
              out_filters,
              kernel_size=kernel_size,
              dilation=dilation,
          ),
          nn.Sigmoid(),
      )
  
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 MaskedSEModule object from inherited base classes: nn with example: obj = MaskedSEModule(inp_filters, se_filters, out_filters, kernel_size, dilation)