code: def get_model(with_pipeline=False):
      """Get a multi-layer perceptron model.
  
      Optionally, put it in a pipeline that scales the data.
  
      """
      model = NeuralNetClassifier(MLPClassifier)
      if with_pipeline:
          model = Pipeline(
              [
                  (
                      "scale",
                      FeatureUnion(
                          [
                              ("minmax", MinMaxScaler()),
                              ("normalize", Normalizer()),
                          ]
                      ),
                  ),
                  (
                      "select",
                      SelectKBest(k=N_FEATURES),
                  ),  # keep input size constant
                  ("net", model),
              ]
          )
      return model
  
imports: import pickle
  import fire
  import numpy
  from sklearn.datasets import make_classification
  from sklearn.feature_selection import SelectKBest
  from sklearn.pipeline import FeatureUnion
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import MinMaxScaler
  from sklearn.preprocessing import Normalizer
  from skorch import NeuralNetClassifier
  import torch
  from torch import nn
  from skorch.helper import parse_args
label_desc: Use image node_id 2 for calling a global function with example usage: get_model(with_pipeline) and returns: model