code: def dummy_minimize(
      func,
      dimensions,
      n_calls=100,
      initial_point_generator="random",
      x0=None,
      y0=None,
      random_state=None,
      verbose=False,
      callback=None,
      model_queue_size=None,
      init_point_gen_kwargs=None,
  ):
      """Random search by uniform sampling within the given bounds.
  
      Parameters
      ----------
      func : callable
          Function to minimize. Should take a single list of parameters
          and return the objective value.
      
          If you have a search-space where all dimensions have names,
          then you can use :func:`skopt.utils.use_named_args` as a decorator
          on your objective function, in order to call it directly
          with the named arguments. See `use_named_args` for an example.
  
      dimensions : list, shape (n_dims,)
          List of search space dimensions.
          Each search dimension can be defined either as
  
          - a `(lower_bound, upper_bound)` tuple (for `Real` or `Integer`
            dimensions),
          - a `(lower_bound, upper_bound, prior)` tuple (for `Real`
            dimensions),
          - as a list of categories (for `Categorical` dimensions), or
          - an instance of a `Dimension` object (`Real`, `Integer` or
            `Categorical`).
  
      n_calls : int, default: 100
          Number of calls to `func` to find the minimum.
  
      initial_point_generator : str, InitialPointGenerator instance, \
              default: `"random"`
          Sets a initial points generator. Can be either
  
          - `"random"` for uniform random numbers,
          - `"sobol"` for a Sobol' sequence,
          - `"halton"` for a Halton sequence,
          - `"hammersly"` for a Hammersly sequence,
          - `"lhs"` for a latin hypercube sequence,
          - `"grid"` for a uniform grid sequence
  
      x0 : list, list of lists or `None`
          Initial input points.
  
          - If it is a list of lists, use it as a list of input points.
          - If it is a list, use it as a single initial input point.
          - If it is `None`, no initial input points are used.
  
      y0 : list, scalar or `None`
          Evaluation of initial input points.
  
          - If it is a list, then it corresponds to evaluations of the function
            at each element of `x0` : the i-th element of `y0` corresponds
            to the function evaluated at the i-th element of `x0`.
          - If it is a scalar, then it corresponds to the evaluation of the
            function at `x0`.
          - If it is None and `x0` is provided, then the function is evaluated
            at each element of `x0`.
  
      random_state : int, RandomState instance, or None (default)
          Set random state to something other than None for reproducible
          results.
  
      verbose : boolean, default: False
          Control the verbosity. It is advised to set the verbosity to True
          for long optimization runs.
  
      callback : callable, list of callables, optional
          If callable then `callback(res)` is called after each call to `func`.
          If list of callables, then each callable in the list is called.
  
      model_queue_size : int or None, default: None
          Keeps list of models only as long as the argument given. In the
          case of None, the list has no capped length.
  
      Returns
      -------
      res : `OptimizeResult`, scipy object
          The optimization result returned as a OptimizeResult object.
          Important attributes are:
  
          - `x` [list]: location of the minimum.
          - `fun` [float]: function value at the minimum.
          - `x_iters` [list of lists]: location of function evaluation for each
            iteration.
          - `func_vals` [array]: function value for each iteration.
          - `space` [Space]: the optimisation space.
          - `specs` [dict]: the call specifications.
          - `rng` [RandomState instance]: State of the random state
            at the end of minimization.
  
          For more details related to the OptimizeResult object, refer
          http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.OptimizeResult.html
  
      .. seealso:: functions :class:`skopt.gp_minimize`,
          :class:`skopt.forest_minimize`, :class:`skopt.gbrt_minimize`
      """
      # all our calls want random suggestions, except if we need to evaluate
      # some initial points
      if x0 is not None and y0 is None:
          n_initial_points = n_calls - len(x0)
      else:
          n_initial_points = n_calls
  
      return base_minimize(
          func,
          dimensions,
          base_estimator="dummy",
          # explicitly set optimizer to sampling as "dummy"
          # minimizer does not provide gradients.
          acq_optimizer="sampling",
          n_calls=n_calls,
          n_initial_points=n_initial_points,
          initial_point_generator=initial_point_generator,
          x0=x0,
          y0=y0,
          random_state=random_state,
          verbose=verbose,
          callback=callback,
          model_queue_size=model_queue_size,
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: dummy_minimize(func, dimensions, n_calls, initial_point_generator, x0, y0, random_state, verbose, callback, model_queue_size, init_point_gen_kwargs) and returns: base_minimize