class_docstr: Implement a EuclideanMean object.
docstr: Performs a forward pass through the network.
  
  Parameters
  ----------
  data : torch.Tensor
      The input data, as a float tensor
  
  Returns:
  -------
  torch.Tensor
      The encoded output, as a float tensor
docstr_tok: ["Performs","a","forward","pass","through","the","network",".","Parameters","--","--","--","--","--","data",":","torch.Tensor","The","input","data",",","as","a","float","tensor","Returns",":","--","--","--","-","torch.Tensor","The","encoded","output",",","as","a","float","tensor"]
code: def forward(self, data: Tensor) -> Tensor:
      """Performs a forward pass through the network.
  
      Parameters
      ----------
      data : torch.Tensor
          The input data, as a float tensor
  
      Returns:
      -------
      torch.Tensor
          The encoded output, as a float tensor
  
      """
      return data.mean(0)
  
file_path: flair/flair/nn/distance/euclidean.py
imports: import torch
  from torch import Tensor, nn
label_desc: Use image node_id 1 for calling the EuclideanMean obj's underlying member method code with example usage: obj.forward(data) and returns: data