class_docstr: Container holding a list of metrics.
  
  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.
class_docstr_tok: ["Container","holding","a","list","of","metrics",".","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","."]
docstr: Compute all metrics and store into a dict.
  
  Parameters
  ----------
  y_true : np.ndarray
      Target matrix or vector
  y_pred : np.ndarray
      Score matrix or vector
  
  Returns
  -------
  dict
      Dict of metrics ({metric_name: metric_value}).
docstr_tok: ["Compute","all","metrics","and","store","into","a","dict",".","Parameters","--","--","--","--","--","y_true",":","np.ndarray","Target","matrix","or","vector","y_pred",":","np.ndarray","Score","matrix","or","vector","Returns","--","--","--","-","dict","Dict","of","metrics","(","{","metric_name",":","metric_value","}",")","."]
code: def __call__(self, y_pred, embedded_x, obf_vars):
      """Compute all metrics and store into a dict.
  
      Parameters
      ----------
      y_true : np.ndarray
          Target matrix or vector
      y_pred : np.ndarray
          Score matrix or vector
  
      Returns
      -------
      dict
          Dict of metrics ({metric_name: metric_value}).
  
      """
      logs = {}
      for metric in self.metrics:
          res = metric(y_pred, embedded_x, obf_vars)
          logs[self.prefix + metric._name] = res
      return logs
  
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 UnsupMetricContainer obj's underlying member method code with example usage: obj.__call__(y_pred, embedded_x, obf_vars) and returns: logs