code: def decide(backend):
      """Decides the Model implementation type.
  
      To add new implementations of Model, add the class in model_interfaces subpackage and
      import-and-return the class in an elif loop as shown in the below method.
      """
      if backend == BackEndTypes.Sklearn:
          # random sampling of CFs
          from dice_ml.model_interfaces.base_model import BaseModel
  
          return BaseModel
  
      elif (
          backend == BackEndTypes.Tensorflow1
          or backend == BackEndTypes.Tensorflow2
      ):
          # Tensorflow 1 or 2 backend
          try:
              import tensorflow  # noqa: F401
          except ImportError:
              raise UserConfigValidationException(
                  "Unable to import tensorflow. Please install tensorflow"
              )
          from dice_ml.model_interfaces.keras_tensorflow_model import (
              KerasTensorFlowModel,
          )
  
          return KerasTensorFlowModel
  
      elif backend == BackEndTypes.Pytorch:
          # PyTorch backend
          try:
              import torch  # noqa: F401
          except ImportError:
              raise UserConfigValidationException(
                  "Unable to import torch. Please install torch from https://pytorch.org/"
              )
          from dice_ml.model_interfaces.pytorch_model import (
              PyTorchModel,
          )
  
          return PyTorchModel
  
      else:
          # all other implementations and frameworks
          backend_model = backend["model"]
          module_name, class_name = backend_model.split(".")
          module = __import__(
              "dice_ml.model_interfaces." + module_name,
              fromlist=[class_name],
          )
          return getattr(module, class_name)
  
imports: import warnings
  from raiutils.exceptions import UserConfigValidationException
  from dice_ml.constants import BackEndTypes, ModelTypes
label_desc: Use image node_id 1 for calling a global function with example usage: decide(backend) and returns: BaseModel, KerasTensorFlowModel, PyTorchModel, getattr