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.
class_docstr_tok: ["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","."]
code: def __init__(self):
      self._name = "rmsle"
      self._maximize = False
  
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 to create a new RMSLE object from inherited base classes: Metric with example: obj = RMSLE()