code: def predictions(
      self,
      learner,
      preproces,
      X_train,
      y_train,
      sample_weight,
      sensitive_features,
      X_validation,
      y_validation,
      sample_weight_validation,
      sensitive_features_validation,
  ):
      y_train_true = y_train
      y_train_predicted = learner.predict(X_train)
      y_validation_true = y_validation
      y_validation_predicted = learner.predict(X_validation)
  
      y_train_true = preproces.inverse_scale_target(y_train_true)
      y_train_predicted = preproces.inverse_scale_target(
          y_train_predicted
      )
      y_validation_true = preproces.inverse_scale_target(
          y_validation_true
      )
      y_validation_predicted = preproces.inverse_scale_target(
          y_validation_predicted
      )
  
      y_validation_columns = []
      if self._ml_task == MULTICLASS_CLASSIFICATION:
          # y_train_true = preproces.inverse_categorical_target(y_train_true)
          # y_validation_true = preproces.inverse_categorical_target(y_validation_true)
          # get columns, omit the last one (it is label)
          y_validation_columns = preproces.prepare_target_labels(
              y_validation_predicted
          ).columns.tolist()[:-1]
      elif self._ml_task == BINARY_CLASSIFICATION:
          class_names = self.preprocessings[-1].get_target_class_names()
          y_validation_columns = "prediction"
          if not ("0" in class_names and "1" in class_names):
              y_validation_columns = f"prediction_0_for_{class_names[0]}_1_for_{class_names[1]}"
      else:
          y_validation_columns = "prediction"
  
      return {
          "y_train_true": y_train_true,
          "y_train_predicted": y_train_predicted,
          "sample_weight": sample_weight,
          "sensitive_features": sensitive_features,
          "y_validation_true": y_validation_true,
          "y_validation_predicted": y_validation_predicted,
          "sample_weight_validation": sample_weight_validation,
          "sensitive_features_validation": sensitive_features_validation,
          "validation_index": X_validation.index,
          "validation_columns": y_validation_columns,
      }
  
imports: import copy
  import gc
  import json
  import logging
  import os
  import sys
  import time
  import uuid
  import zipfile
  import numpy
  import pandas
  from supervised.algorithms.factory import AlgorithmFactory
  from supervised.algorithms.registry import BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, REGRESSION, AlgorithmsRegistry
  from supervised.callbacks.callback_list import CallbackList
  from supervised.exceptions import AutoMLException
  from supervised.preprocessing.exclude_missing_target import ExcludeRowsMissingTarget
  from supervised.preprocessing.preprocessing import Preprocessing
  from supervised.utils.additional_metrics import AdditionalMetrics
  from supervised.utils.config import LOG_LEVEL
  from supervised.utils.metric import Metric
  from supervised.validation.validation_step import ValidationStep
  from supervised.utils.jsonencoder import MLJSONEncoder
  import joblib
  import optuna
  from supervised.tuner.optuna.tuner import OptunaTuner
  from supervised.utils.config import mem
  from supervised.utils.learning_curves import LearningCurves
label_desc: Use image node_id 3 for calling the ModelFramework obj's underlying member method code with example usage: obj.predictions(learner, preproces, X_train, y_train, sample_weight, sensitive_features, X_validation, y_validation, sample_weight_validation, sensitive_features_validation) and returns: dict