class_docstr: A model bridge for using purely random 'models'.
  Data and optimization configs are not required.
  
  This model bridge interfaces with RandomModel.
  
  Attributes:
      model: A RandomModel used to generate candidates
          (note: this an awkward use of the word 'model').
      parameters: Params found in search space on modelbridge init.
class_docstr_tok: ["A","model","bridge","for","using","purely","random","'models","'",".","Data","and","optimization","configs","are","not","required",".","This","model","bridge","interfaces","with","RandomModel",".","Attributes",":","model",":","A","RandomModel","used","to","generate","candidates","(","note",":","this","an","awkward","use","of","the","word","'model","'",")",".","parameters",":","Params","found","in","search","space","on","modelbridge","init","."]
docstr: Apply terminal transform, predict, and reverse terminal transform on
  output.
docstr_tok: ["Apply","terminal","transform",",","predict",",","and","reverse","terminal","transform","on","output","."]
code: def _predict(
      self, observation_features: List[ObservationFeatures]
  ) -> List[ObservationData]:
      """Apply terminal transform, predict, and reverse terminal transform on
      output.
      """
      raise NotImplementedError(
          "RandomModelBridge does not support prediction."
      )
  
file_path: Ax/ax/modelbridge/random.py
imports: from typing import Dict, List, Optional
  from ax.core.experiment import Experiment
  from ax.core.observation import Observation, ObservationData, ObservationFeatures
  from ax.core.optimization_config import OptimizationConfig
  from ax.core.search_space import SearchSpace
  from ax.modelbridge.base import GenResults, ModelBridge
  from ax.modelbridge.modelbridge_utils import extract_parameter_constraints, extract_search_space_digest, get_fixed_features, parse_observation_features, transform_callback
  from ax.models.random.base import RandomModel
  from ax.models.types import TConfig
label_desc: Use image node_id 3 for calling the RandomModelBridge obj's underlying member method code with example usage: obj._predict(observation_features) without return types