code: def _resolve_model(future_model, **kwargs):
      future_model.poll()
      rest_ver = (
          kwargs["_rest_version"] if "_rest_version" in kwargs else 3
      )
      model_json = h2o.api(
          "GET /%d/Models/%s" % (rest_ver, future_model.job.dest_key)
      )["models"][0]
  
      model_type = model_json["output"]["model_category"]
      if model_type == "Binomial":
          model = H2OBinomialModel(
              future_model.job.dest_key, model_json
          )
      elif model_type == "Clustering":
          model = H2OClusteringModel(
              future_model.job.dest_key, model_json
          )
      elif model_type == "Regression":
          model = H2ORegressionModel(
              future_model.job.dest_key, model_json
          )
      elif model_type == "Multinomial":
          model = H2OMultinomialModel(
              future_model.job.dest_key, model_json
          )
      elif model_type == "Ordinal":
          model = H2OOrdinalModel(future_model.job.dest_key, model_json)
      elif model_type == "AutoEncoder":
          model = H2OAutoEncoderModel(
              future_model.job.dest_key, model_json
          )
      elif model_type == "DimReduction":
          model = H2ODimReductionModel(
              future_model.job.dest_key, model_json
          )
      elif model_type == "WordEmbedding":
          model = H2OWordEmbeddingModel(
              future_model.job.dest_key, model_json
          )
      else:
          raise NotImplementedError(model_type)
      return model
  
file_path: h2o-3/h2o-py/h2o/model/model_builder.py
imports: from h2o.utils.compatibility import *
  import h2o
  from ..frame import H2OFrame
  from ..job import H2OJob
  from .model_future import H2OModelFuture
  from .models.dim_reduction import H2ODimReductionModel
  from .models.autoencoder import H2OAutoEncoderModel
  from .models.multinomial import H2OMultinomialModel
  from .models.ordinal import H2OOrdinalModel
  from .models.regression import H2ORegressionModel
  from .models.binomial import H2OBinomialModel
  from .models.clustering import H2OClusteringModel
  from .models.word_embedding import H2OWordEmbeddingModel
label_desc: Use image node_id 3 for calling a global function with example usage: _resolve_model(future_model) and returns: model