class_docstr: AbstractModel which returns random values on a call to `fit`.
code: def _predict(
      self,
      X: np.ndarray,
      covariance_type: str | None = "diagonal",
  ) -> tuple[np.ndarray, np.ndarray | None]:
      if covariance_type != "diagonal":
          raise ValueError(
              "`covariance_type` can only take `diagonal` for this model."
          )
  
      if not isinstance(X, np.ndarray):
          raise NotImplementedError("X has to be of type np.ndarray.")
  
      return self._rng.rand(len(X), 1), self._rng.rand(len(X), 1)
  
imports: from __future__ import annotations
  import numpy
  from smac.model.abstract_model import AbstractModel
  from smac.utils.logging import get_logger
label_desc: Use image node_id 2 for calling the RandomModel obj's underlying member method code with example usage: obj._predict(X, covariance_type) and returns: self,  self