code: def create_hammersley_samples(order, dim=1, burnin=-1, primes=()):
      
      if dim == 1:
          return create_halton_samples(
              order=order, dim=1, burnin=burnin, primes=primes)
      out = numpy.empty((dim, order), dtype=float)
      out[:dim-1] = create_halton_samples(
          order=order, dim=dim-1, burnin=burnin, primes=primes)
      out[dim-1] = numpy.linspace(0, 1, order+2)[1:-1]
      return out
docs: Create samples from the Hammersley set.
  
      For ``dim == 1`` the sequence falls back to Van Der Corput sequence.
  
      Args:
          order (int):
              The order of the Hammersley sequence. Defines the number of samples.
          dim (int):
              The number of dimensions in the Hammersley sequence.
          burnin (int):
              Skip the first ``burnin`` samples. If negative, the maximum of
              ``primes`` is used.
          primes (tuple):
              The (non-)prime base to calculate values along each axis. If
              empty, growing prime values starting from 2 will be used.
  
      Returns:
          (numpy.ndarray):
              Hammersley set with ``shape == (dim, order)``.