class_docstr: Root Mean squared logarithmic error regression loss.
  Scikit-implementation:
  https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_log_error.html
  Note: In order to avoid error, negative predictions are clipped to 0.
  This means that you should clip negative predictions manually after calling predict.
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docstr: Compute RMSLE of predictions.
  
  Parameters
  ----------
  y_true : np.ndarray
      Target matrix or vector
  y_score : np.ndarray
      Score matrix or vector
  
  Returns
  -------
  float
      RMSLE of predictions vs targets.
docstr_tok: ["Compute","RMSLE","of","predictions",".","Parameters","--","--","--","--","--","y_true",":","np.ndarray","Target","matrix","or","vector","y_score",":","np.ndarray","Score","matrix","or","vector","Returns","--","--","--","-","float","RMSLE","of","predictions","vs","targets","."]
code: def __call__(self, y_true, y_score):
      """
      Compute RMSLE of predictions.
  
      Parameters
      ----------
      y_true : np.ndarray
          Target matrix or vector
      y_score : np.ndarray
          Score matrix or vector
  
      Returns
      -------
      float
          RMSLE of predictions vs targets.
      """
      y_score = np.clip(y_score, a_min=0, a_max=None)
      return np.sqrt(mean_squared_log_error(y_true, y_score))
  
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 RMSLE obj's underlying member method code with example usage: obj.__call__(y_true, y_score) and returns: np