code: def _func_predict_proba_partial(model, input_data, **kwargs):
      """
      Whole dataset inference with part of the model (trees at disposal locally).
      Transfer dataset instead of model. Interesting when model is larger
      than dataset.
      """
      X = concatenate(input_data)
      with using_output_type("cupy"):
          prediction = model.predict_proba(X, **kwargs)
          return cp.expand_dims(prediction, axis=1)
  
imports: from cuml.fil.fil import TreeliteModel
  from cuml.dask.common.utils import get_client, wait_and_raise_from_futures
  from cuml.dask.common.input_utils import DistributedDataHandler, concatenate
  from dask.distributed import Future
  from collections.abc import Iterable
  from cuml import using_output_type
  import warnings
  from cuml.internals.safe_imports import gpu_only_import
  import dask
  import json
  import math
  from cuml.internals.safe_imports import cpu_only_import
label_desc: Use image node_id 3 for calling a global function with example usage: _func_predict_proba_partial(model, input_data) and returns: cp