code: def romb(y, dx=1.0, axis=-1, show=False):
      """
      Romberg integration using samples of a function.
  
      Parameters
      ----------
      y : array_like
          A vector of ``2**k + 1`` equally-spaced samples of a function.
      dx : float, optional
          The sample spacing. Default is 1.
      axis : int, optional
          The axis along which to integrate. Default is -1 (last axis).
      show : bool, optional
          When `y` is a single 1-D array, then if this argument is True
          print the table showing Richardson extrapolation from the
          samples. Default is False.
  
      Returns
      -------
      romb : ndarray
          The integrated result for `axis`.
  
      See Also
      --------
      quad : adaptive quadrature using QUADPACK
      romberg : adaptive Romberg quadrature
      quadrature : adaptive Gaussian quadrature
      fixed_quad : fixed-order Gaussian quadrature
      dblquad : double integrals
      tplquad : triple integrals
      simpson : integrators for sampled data
      cumulative_trapezoid : cumulative integration for sampled data
      ode : ODE integrators
      odeint : ODE integrators
  
      Examples
      --------
      >>> from scipy import integrate
      >>> import numpy as np
      >>> x = np.arange(10, 14.25, 0.25)
      >>> y = np.arange(3, 12)
  
      >>> integrate.romb(y)
      56.0
  
      >>> y = np.sin(np.power(x, 2.5))
      >>> integrate.romb(y)
      -0.742561336672229
  
      >>> integrate.romb(y, show=True)
      Richardson Extrapolation Table for Romberg Integration
      ======================================================
      -0.81576
       4.63862  6.45674
      -1.10581 -3.02062 -3.65245
      -2.57379 -3.06311 -3.06595 -3.05664
      -1.34093 -0.92997 -0.78776 -0.75160 -0.74256
      ======================================================
      -0.742561336672229  # may vary
  
      """
      y = np.asarray(y)
      nd = len(y.shape)
      Nsamps = y.shape[axis]
      Ninterv = Nsamps - 1
      n = 1
      k = 0
      while n < Ninterv:
          n <<= 1
          k += 1
      if n != Ninterv:
          raise ValueError(
              "Number of samples must be one plus a "
              "non-negative power of 2."
          )
  
      R = {}
      slice_all = (slice(None),) * nd
      slice0 = tupleset(slice_all, axis, 0)
      slicem1 = tupleset(slice_all, axis, -1)
      h = Ninterv * np.asarray(dx, dtype=float)
      R[(0, 0)] = (y[slice0] + y[slicem1]) / 2.0 * h
      slice_R = slice_all
      start = stop = step = Ninterv
      for i in range(1, k + 1):
          start >>= 1
          slice_R = tupleset(slice_R, axis, slice(start, stop, step))
          step >>= 1
          R[(i, 0)] = 0.5 * (
              R[(i - 1, 0)] + h * y[slice_R].sum(axis=axis)
          )
          for j in range(1, i + 1):
              prev = R[(i, j - 1)]
              R[(i, j)] = prev + (prev - R[(i - 1, j - 1)]) / (
                  (1 << (2 * j)) - 1
              )
          h /= 2.0
  
      if show:
          if not np.isscalar(R[(0, 0)]):
              print(
                  "*** Printing table only supported for integrals"
                  + " of a single data set."
              )
          else:
              try:
                  precis = show[0]
              except (TypeError, IndexError):
                  precis = 5
              try:
                  width = show[1]
              except (TypeError, IndexError):
                  width = 8
              formstr = "%%%d.%df" % (width, precis)
  
              title = "Richardson Extrapolation Table for Romberg Integration"
              print(title, "=" * len(title), sep="\n", end="\n")
              for i in range(k + 1):
                  for j in range(i + 1):
                      print(formstr % R[(i, j)], end=" ")
                  print()
              print("=" * len(title))
  
      return R[(k, k)]
  
imports: from __future__ import annotations
  from typing import TYPE_CHECKING, Callable, Any, cast
  import numpy
  import numpy.typing
  import math
  import warnings
  from collections import namedtuple
  from scipy.special import roots_legendre
  from scipy.special import gammaln, logsumexp
  from scipy._lib._util import _rng_spawn
  from scipy._lib.deprecation import _NoValue, _deprecate_positional_args, _deprecated
label_desc: Use image node_id 19 for calling a global function with example usage: romb(y, dx, axis, show) and returns: R