class_docstr: This component is a DAG.
class_docstr_tok: ["This","component","is","a","DAG","."]
code: def __init__(self, models_paths: list):
      super().__init__()
      # Step 1: Create a work to get the data.
      self.data_collector = GetDataWork()
  
      # Step 2: Create a tracer component. This is used to execute python script
      # and collect any outputs from its globals as Payloads.
      self.processing = TracerPythonScript(
          get_path("processing.py"),
          outputs=["X_train", "X_test", "y_train", "y_test"],
      )
  
      # Step 3: Create the work to train the models_paths in parallel.
      self.dict = Dict(
          **{
              model_path.split(".")[-1]: ModelWork(
                  model_path, parallel=True
              )
              for model_path in models_paths
          }
      )
  
      # Step 4: Some element to track components progress.
      self.has_completed = False
      self.metrics = {}
  
imports: import os
  from importlib import import_module
  import numpy
  import pandas
  from lightning.app import LightningApp, LightningFlow, LightningWork
  from lightning.app.components import TracerPythonScript
  from lightning.app.storage import Payload
  from lightning.app.structures import Dict, List
  from sklearn import datasets
  from sklearn.metrics import mean_squared_error
label_desc: Use image node_id 1 to create a new DAG object from inherited base classes: LightningFlow with example: obj = DAG(models_paths)