text: import torch
  import torch.nn as nn
  
  from .py_utils import exkp, CTLoss, _neg_loss, convolution, residual
  
  def make_pool_layer(dim):
      return nn.Sequential()
  
  def make_hg_layer(kernel, dim0, dim1, mod, layer=convolution, **kwargs):
      layers  = [layer(kernel, dim0, dim1, stride=2)]
      layers += [layer(kernel, dim1, dim1) for _ in range(mod - 1)]
      return nn.Sequential(*layers)
  
  class model(exkp):
      def __init__(self, db):
          n       = 5
          dims    = [256, 256, 384, 384, 384, 512]
          modules = [2, 2, 2, 2, 2, 4]
          out_dim = 1
  
          super(model, self).__init__(
              n, 2, dims, modules, out_dim,
              make_tl_layer=None,
              make_br_layer=None,
              make_pool_layer=make_pool_layer,
              make_hg_layer=make_hg_layer,
              kp_layer=residual, cnv_dim=256
          )
  
  loss = CTLoss(focal_loss=_neg_loss)
  