code: def pipeline_with_pca() -> Pipeline:
      node_scaling = PipelineNode("scaling")
      node_pca = PipelineNode("pca", nodes_from=[node_scaling])
      node_lda = PipelineNode("lda", nodes_from=[node_scaling])
      node_final = PipelineNode("rf", nodes_from=[node_pca, node_lda])
  
      pipeline = Pipeline(node_final)
  
      return pipeline
  
imports: import os
  import numpy
  from sklearn.datasets import load_iris, make_classification
  from sklearn.metrics import roc_auc_score
  from examples.simple.classification.image_classification_problem import run_image_classification_problem
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.data.supplementary_data import SupplementaryData
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.tasks import Task, TaskTypesEnum
  from test.integration.models.test_model import classification_dataset_with_redundant_features
label_desc: Use image node_id 2 for calling a global function with example usage: pipeline_with_pca() and returns: pipeline