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","."]
code: def __post_init__(self):
      self.metrics = Metric.get_metrics_by_names(self.metric_names)
      self.names = [self.prefix + name for name in self.metric_names]
  
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 1 for calling the UnsupMetricContainer obj's underlying member method code with example usage: obj.__post_init__() without return types