code: def rmsle(y_true, y_pred):
      return (
          "RMSLE",
          np.sqrt(
              np.mean(np.power(np.log1p(y_pred) - np.log1p(y_true), 2))
          ),
          False,
      )
  
file_path: LightGBM/examples/python-guide/sklearn_example.py
imports: from pathlib import Path
  import numpy
  import pandas
  from sklearn.metrics import mean_squared_error
  from sklearn.model_selection import GridSearchCV
  import lightgbm
label_desc: Use image node_id 1 for calling a global function with example usage: rmsle(y_true, y_pred) and returns: str,  np,  False