code: def interpret(
      self,
      X_train,
      y_train,
      X_validation,
      y_validation,
      model_file_path,
      learner_name,
      target_name=None,
      class_names=None,
      metric_name=None,
      ml_task=None,
      explain_level=2,
  ):
      super(LinearAlgorithm, self).interpret(
          X_train,
          y_train,
          X_validation,
          y_validation,
          model_file_path,
          learner_name,
          target_name,
          class_names,
          metric_name,
          ml_task,
          explain_level,
      )
      if explain_level == 0:
          return
      if X_train.shape[1] > 100:
          # if too many columns, skip this step
          return
      coefs = self.model.coef_
      intercept = self.model.intercept_
      if self.params["ml_task"] == BINARY_CLASSIFICATION:
          df = pd.DataFrame(
              {
                  "feature": ["intercept"] + X_train.columns.tolist(),
                  "weight": [intercept[0]] + list(coefs[0, :]),
              }
          )
          df.to_csv(
              os.path.join(
                  model_file_path, f"{learner_name}_coefs.csv"
              ),
              index=False,
          )
      elif self.params["ml_task"] == MULTICLASS_CLASSIFICATION:
          classes = list(class_names)
          if isinstance(class_names, dict):
              classes = class_names.values()
          if len(classes) > 20:
              # if there are too many classes, skip this step
              return
          df = pd.DataFrame(
              np.transpose(np.column_stack((intercept, coefs))),
              index=["intercept"] + X_train.columns.tolist(),
              columns=classes,
          )
          df.to_csv(
              os.path.join(
                  model_file_path, f"{learner_name}_coefs.csv"
              ),
              index=True,
          )
  
imports: import logging
  import os
  import warnings
  import numpy
  import pandas
  import sklearn
  from sklearn.base import ClassifierMixin, RegressorMixin
  from sklearn.exceptions import ConvergenceWarning
  from sklearn.linear_model import LinearRegression, LogisticRegression
  from supervised.algorithms.algorithm import BaseAlgorithm
  from supervised.algorithms.registry import BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, REGRESSION, AlgorithmsRegistry
  from supervised.algorithms.sklearn import SklearnAlgorithm
  from supervised.utils.config import LOG_LEVEL
label_desc: Use image node_id 4 for calling the LinearAlgorithm obj's underlying member method code with example usage: obj.interpret(X_train, y_train, X_validation, y_validation, model_file_path, learner_name, target_name, class_names, metric_name, ml_task, explain_level) without return types