class_docstr: 128-dimensional SOSNet model definition for 32x32 patches.
  
  This is based on the original code from paper
  "SOSNet:Second Order Similarity Regularization for Local Descriptor Learning".
  
  Args:
      pretrained: Download and set pretrained weights to the model.
  
  Shape:
      - Input: :math:`(B, 1, 32, 32)`
      - Output: :math:`(B, 128)`
  
  Examples:
      >>> input = torch.rand(8, 1, 32, 32)
      >>> sosnet = SOSNet()
      >>> descs = sosnet(input) # 8x128
code: def forward(
      self, input: torch.Tensor, eps: float = 1e-10
  ) -> torch.Tensor:
      KORNIA_CHECK_SHAPE(input, ["B", "1", "32", "32"])
      descr = self.desc_norm(self.layers(input) + eps)
      descr = descr.view(descr.size(0), -1)
      return descr
  
file_path: kornia/kornia/feature/sosnet.py
imports: from typing import Dict
  import torch
  from torch import nn
  from kornia.core.check import KORNIA_CHECK_SHAPE
  from kornia.utils.helpers import map_location_to_cpu
label_desc: Use image node_id 2 for calling the SOSNet obj's underlying member method code with example usage: obj.forward(input, eps) and returns: descr