code: def sample_unif(args):
      
  
      # Unzipping.
      (u, loglstar, axes, scale,
       prior_transform, loglikelihood, kwargs) = args
  
      # Evaluate.
      v = prior_transform(np.array(u))
      logl = loglikelihood(np.array(v))
      nc = 1
      blob = None
  
      return u, v, logl, nc, blob
docs: Evaluate a new point sampled uniformly from a bounding proposal
      distribution. Parameters are zipped within `args` to utilize
      `pool.map`-style functions.
  
      Parameters
      ----------
      u : `~numpy.ndarray` with shape (npdim,)
          Position of the initial sample.
  
      loglstar : float
          Ln(likelihood) bound. **Not applicable here.**
  
      axes : `~numpy.ndarray` with shape (ndim, ndim)
          Axes used to propose new points. **Not applicable here.**
  
      scale : float
          Value used to scale the provided axes. **Not applicable here.**
  
      prior_transform : function
          Function transforming a sample from the a unit cube to the parameter
          space of interest according to the prior.
  
      loglikelihood : function
          Function returning ln(likelihood) given parameters as a 1-d `~numpy`
          array of length `ndim`.
  
      kwargs : dict
          A dictionary of additional method-specific parameters.
          **Not applicable here.**
  
      Returns
      -------
      u : `~numpy.ndarray` with shape (npdim,)
          Position of the final proposed point within the unit cube. **For
          uniform sampling this is the same as the initial input position.**
  
      v : `~numpy.ndarray` with shape (ndim,)
          Position of the final proposed point in the target parameter space.
  
      logl : float
          Ln(likelihood) of the final proposed point.
  
      nc : int
          Number of function calls used to generate the sample. For uniform
          sampling this is `1` by construction.
  
      blob : dict
          Collection of ancillary quantities used to tune :data:`scale`. **Not
          applicable for uniform sampling.**