class_docstr: <a id="ClassificationHead"></a>
  
  ## Classification Head
  
  They do average pooling (taking the mean of all patch embeddings) and a final linear transformation
  to predict the log-probabilities of the image classes.
class_docstr_tok: ["<","a","id=","''","ClassificationHead","''",">","<","\/a",">","#","#","Classification","Head","They","do","average","pooling","(","taking","the","mean","of","all","patch","embeddings",")","and","a","final","linear","transformation","to","predict","the","log-probabilities","of","the","image","classes","."]
code: def forward(self, x: torch.Tensor):
      # Average pooling
      x = self.pool(x)
      # Get the embedding, `x` will have shape `[batch_size, d_model, 1, 1]`
      x = x[:, :, 0, 0]
      # Linear layer
      x = self.linear(x)
  
      #
      return x
  
file_path: annotated_deep_learning_paper_implementations/labml_nn/conv_mixer/__init__.py
imports: import torch
  from torch import nn
  from labml_helpers.module import Module
  from labml_nn.utils import clone_module_list
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: x