code: def compute(
      algorithm,
      X_train,
      y_train,
      X_validation,
      y_validation,
      model_file_path,
      learner_name,
      class_names,
      ml_task,
  ):
      if not PlotSHAP.is_available(
          algorithm, X_train, y_train, ml_task
      ):
          return
      try:
          with warnings.catch_warnings():
              warnings.simplefilter("ignore")
              explainer = PlotSHAP.get_explainer(algorithm, X_train)
              X_vald, y_vald = PlotSHAP.get_sample(
                  X_validation, y_validation
              )
              shap_values = explainer.shap_values(X_vald)
  
          # fix problem with 1 or 2 dimensions for binary classification
          expected_value = explainer.expected_value
          if ml_task == BINARY_CLASSIFICATION and isinstance(
              shap_values, list
          ):
              shap_values = shap_values[1]
              expected_value = explainer.expected_value[1]
  
          # Summary SHAP plot
          PlotSHAP.summary(
              shap_values,
              X_vald,
              model_file_path,
              learner_name,
              class_names,
          )
          # Dependence SHAP plots
          if ml_task == MULTICLASS_CLASSIFICATION:
              for t in np.unique(y_vald):
                  PlotSHAP.dependence(
                      shap_values[t],
                      X_vald,
                      model_file_path,
                      learner_name,
                      f"_class_{class_names[t]}",
                  )
          else:
              PlotSHAP.dependence(
                  shap_values, X_vald, model_file_path, learner_name
              )
  
          # Decision SHAP plots
          df_preds = PlotSHAP.get_predictions(
              algorithm, X_vald, y_vald, ml_task
          )
  
          if ml_task == REGRESSION:
              PlotSHAP.decisions_regression(
                  df_preds,
                  shap_values,
                  expected_value,
                  X_vald,
                  y_vald,
                  model_file_path,
                  learner_name,
              )
          elif ml_task == BINARY_CLASSIFICATION:
              PlotSHAP.decisions_binary(
                  df_preds,
                  shap_values,
                  expected_value,
                  X_vald,
                  y_vald,
                  model_file_path,
                  learner_name,
              )
          else:
              PlotSHAP.decisions_multiclass(
                  df_preds,
                  shap_values,
                  expected_value,
                  X_vald,
                  y_vald,
                  model_file_path,
                  learner_name,
                  class_names,
              )
      except Exception as e:
          pass
  
imports: import logging
  import os
  import matplotlib.pyplot
  import numpy
  import pandas
  import shap
  from sklearn.preprocessing import OneHotEncoder
  from supervised.algorithms.registry import BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, REGRESSION
  from supervised.utils.config import LOG_LEVEL
  import warnings
label_desc: Use image node_id 7 for calling the PlotSHAP obj's underlying member method code with example usage: obj.compute(algorithm, X_train, y_train, X_validation, y_validation, model_file_path, learner_name, class_names, ml_task) without return types