code: def __init__(
      self,
      num_outputs,
      in_channels,
      mid_channels,
      num_layers=1,
      kernel_size=3,
      padding=1,
      dropout_ratio=0.0,
      dropout_indx=0,
      norm_layer=nn.BatchNorm2d,
  ):
      super(SepConvHead, self).__init__()
  
      sepconvhead = []
  
      for i in range(num_layers):
          sepconvhead.append(
              SeparableConv2d(
                  in_channels=in_channels if i == 0 else mid_channels,
                  out_channels=mid_channels,
                  dw_kernel=kernel_size,
                  dw_padding=padding,
                  norm_layer=norm_layer,
                  activation="relu",
              )
          )
          if dropout_ratio > 0 and dropout_indx == i:
              sepconvhead.append(nn.Dropout(dropout_ratio))
  
      sepconvhead.append(
          nn.Conv2d(
              in_channels=mid_channels,
              out_channels=num_outputs,
              kernel_size=1,
              padding=0,
          )
      )
  
      self.layers = nn.Sequential(*sepconvhead)
  
imports: import math
  import torch
  import numbers
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new SepConvHead object from inherited base classes: nn with example: obj = SepConvHead(num_outputs, in_channels, mid_channels, num_layers, kernel_size, padding, dropout_ratio, dropout_indx, norm_layer)