code: def given_saved_pipeline(tmpdir: LocalPath) -> Pipeline:
      path = create_root_path(tmpdir, True)
      p = (
          Pipeline([])
          .set_name(ROOT)
          .with_context(CX(tmpdir))
          .with_context(CX(tmpdir))
      )
      dump(p, path)
  
      pipeline_2 = (
          Pipeline([]).set_name(PIPELINE_2).with_context(CX(tmpdir))
      )
      pipeline_2.sub_steps_savers = [
          (SOME_STEPS[0], []),
          (SOME_STEPS[1], []),
      ]
      dump(pipeline_2, create_pipeline2_path(tmpdir, True))
  
      given_saved_some_step(
          multiply_by=2,
          step_no=0,
          path=create_some_step_path(
              tmpdir, step_no=0, create_dir=True
          ),
      )
      given_saved_some_step(
          multiply_by=4,
          step_no=1,
          path=create_some_step_path(
              tmpdir, step_no=1, create_dir=True
          ),
      )
      given_saved_some_step(
          multiply_by=6,
          step_no=2,
          path=create_some_step_path(
              tmpdir, step_no=2, create_dir=True
          ),
      )
  
      p = create_pipeline(tmpdir)
  
      return p
  
imports: import os
  import numpy
  from joblib import dump
  from py._path.local import LocalPath
  from pprint import pprint
  from neuraxle.hyperparams.space import RecursiveDict
  from neuraxle.base import CX, StepWithContext, TruncableJoblibStepSaver
  from neuraxle.pipeline import Pipeline
  from neuraxle.steps.numpy import MultiplyByN
label_desc: Use image node_id 10 for calling a global function with example usage: given_saved_pipeline(tmpdir) and returns: p