code: def initialize_ax_client(self):
      self.ax_client = AxClient()
      self.ax_client.create_experiment(
          parameters=[
              {
                  "name": "amount",
                  "type": "range",
                  "bounds": [0.05, 0.15],
                  "value_type": "float",
              }
          ],
          objective_name="test_accuracy",
      )
  
imports: import argparse
  import copy
  import os
  import shutil
  import tempfile
  from pathlib import Path
  import lightning
  import torch
  from ax.service.ax_client import AxClient
  from mnist import LightningMNISTClassifier, MNISTDataModule
  from prettytable import PrettyTable
  from torch.nn.utils import prune
  import mlflow.pytorch
  from mlflow.tracking.artifact_utils import _download_artifact_from_uri
label_desc: Use image node_id 4 for calling the IterativePrune obj's underlying member method code with example usage: obj.initialize_ax_client() without return types