class_docstr: CVLearner
code: def forward(self, corr):
      # Encode correlation from each layer (Squeezing building blocks)
      out_block1 = self.block1(corr)
      out_block2 = self.block2(out_block1)
      out_block3 = self.block3(out_block2)
      out_block4 = self.block4(out_block3)
  
      # Predict logits with the encoded 4D-tensor
      bsz, ch, _, _, _, _ = out_block4.size()
      out_block4_pooled = out_block4.view(bsz, ch, -1).mean(-1)
      logits = self.mlp(out_block4_pooled).squeeze(-1).squeeze(-1)
  
      return logits
  
file_path: towhee/towhee/models/cvnet/cvnet_block.py
imports: import torch
  from torch import nn
  import torch.nn.functional
  from torch.nn.functional import interpolate
  from towhee.models.layers.conv4d import CenterPivotConv4d
  from towhee.models.cvnet.cvnet_utils import Geometry
  import math
label_desc: Use image node_id 4 for calling the CVLearner obj's underlying member method code with example usage: obj.forward(corr) and returns: logits