class_docstr: Unsupervised metric
class_docstr_tok: ["Unsupervised","metric"]
docstr: Compute MSE (Mean Squared Error) of predictions.
  
  Parameters
  ----------
  y_pred : torch.Tensor or np.array
      Reconstructed prediction (with embeddings)
  embedded_x : torch.Tensor
      Original input embedded by network
  obf_vars : torch.Tensor
      Binary mask for obfuscated variables.
      1 means the variables was obfuscated so reconstruction is based on this.
  
  Returns
  -------
  float
      MSE of predictions vs targets.
docstr_tok: ["Compute","MSE","(","Mean","Squared","Error",")","of","predictions",".","Parameters","--","--","--","--","--","y_pred",":","torch.Tensor","or","np.array","Reconstructed","prediction","(","with","embeddings",")","embedded_x",":","torch.Tensor","Original","input","embedded","by","network","obf_vars",":","torch.Tensor","Binary","mask","for","obfuscated","variables",".","1","means","the","variables","was","obfuscated","so","reconstruction","is","based","on","this",".","Returns","--","--","--","-","float","MSE","of","predictions","vs","targets","."]
code: def __call__(self, y_pred, embedded_x, obf_vars):
      """
      Compute MSE (Mean Squared Error) of predictions.
  
      Parameters
      ----------
      y_pred : torch.Tensor or np.array
          Reconstructed prediction (with embeddings)
      embedded_x : torch.Tensor
          Original input embedded by network
      obf_vars : torch.Tensor
          Binary mask for obfuscated variables.
          1 means the variables was obfuscated so reconstruction is based on this.
  
      Returns
      -------
      float
          MSE of predictions vs targets.
      """
      return UnsupervisedLossNumpy(y_pred, embedded_x, obf_vars)
  
imports: from dataclasses import dataclass
  from typing import List
  import numpy
  from sklearn.metrics import roc_auc_score, mean_squared_error, mean_absolute_error, accuracy_score, log_loss, balanced_accuracy_score, mean_squared_log_error
  import torch
label_desc: Use image node_id 2 for calling the UnsupervisedNumpyMetric obj's underlying member method code with example usage: obj.__call__(y_pred, embedded_x, obf_vars) and returns: UnsupervisedLossNumpy