class_docstr: Module to be used as a classification head.
  
  Args:
      embed_size: the logits tensor coming from the networks.
      num_classes: an integer representing the numbers of classes to classify.
  
  Example:
      >>> feat = torch.rand(1, 256, 256)
      >>> head = ClassificationHead(256, 10)
      >>> head(feat).shape
      torch.Size([1, 10])
class_docstr_tok: ["Module","to","be","used","as","a","classification","head",".","Args",":","embed_size",":","the","logits","tensor","coming","from","the","networks",".","num_classes",":","an","integer","representing","the","numbers","of","classes","to","classify",".","Example",":",">",">",">","feat","=","torch.rand","(","1",",","256",",","256",")",">",">",">","head","=","ClassificationHead","(","256",",","10",")",">",">",">","head","(","feat",")",".shape","torch.Size","(","[","1",",","10","]",")"]
code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      out = x.mean(-2)
      return self.linear(self.norm(out))
  
imports: import torch
  from torch import nn
label_desc: Use image node_id 2 for calling the ClassificationHead obj's underlying member method code with example usage: obj.forward(x) and returns: self