class_docstr: Helper class to make use of optimized parameters in both FIL and
  experimental FIL through a uniform interface
code: def __init__(
      self,
      fil_model,
      optimal_chunk_size,
      experimental,
      infer_type="default",
  ):
      self.fil_model = fil_model
      self.predict_kwargs = {}
      if experimental:
          self.predict_kwargs["chunk_size"] = optimal_chunk_size
      self.infer_type = infer_type
  
imports: from cuml.manifold import UMAP
  from cuml.benchmark import datagen
  from cuml.common.device_selection import using_device_type
  from cuml.internals.device_type import DeviceType
  from cuml.internals.global_settings import GlobalSettings
  from cuml.internals.safe_imports import cpu_only_import, gpu_only_import, gpu_only_import_from, safe_import
  import sklearn.ensemble
  import pickle
  import os
  import cuml
  from cuml.internals import input_utils
  from time import perf_counter
label_desc: Use image node_id 1 to create a new OptimizedFilWrapper object  with example: obj = OptimizedFilWrapper(fil_model, optimal_chunk_size, experimental, infer_type)