code: def __init__(self, globals_dict: dict) -> None:
      def root_mean_squared_log_error(
          y_true,
          y_pred,
          *,
          sample_weight=None,
          multioutput="uniform_average",
      ):
          return np.sqrt(
              metrics.mean_squared_log_error(
                  np.abs(y_true),
                  np.abs(y_pred),
                  sample_weight=sample_weight,
                  multioutput=multioutput,
              )
          )
  
      super().__init__(
          id="rmsle",
          name="RMSLE",
          score_func=root_mean_squared_log_error,
          scorer=pycaret.internal.metrics.make_scorer_with_error_score(
              root_mean_squared_log_error,
              error_score=0.0,
              greater_is_better=False,
          ),
          greater_is_better=False,
      )
  
imports: from typing import Any, Dict, Optional, Union
  import numpy
  from sklearn import metrics
  from sklearn.metrics._regression import _check_reg_targets
  from sklearn.metrics._scorer import _BaseScorer
  from sklearn.utils.validation import check_consistent_length
  import pycaret.containers.base_container
  import pycaret.internal.metrics
  from pycaret.containers.metrics.base_metric import MetricContainer
label_desc: Use image node_id 1 to create a new RMSLEMetricContainer object from inherited base classes: RegressionMetricContainer with example: obj = RMSLEMetricContainer(globals_dict)