code: def resample(x, num, t=None, axis=0, window=None, domain="time"):
      """
      Resample `x` to `num` samples using Fourier method along the given axis.
  
      The resampled signal starts at the same value as `x` but is sampled
      with a spacing of ``len(x) / num * (spacing of x)``.  Because a
      Fourier method is used, the signal is assumed to be periodic.
  
      Parameters
      ----------
      x : array_like
          The data to be resampled.
      num : int
          The number of samples in the resampled signal.
      t : array_like, optional
          If `t` is given, it is assumed to be the sample positions
          associated with the signal data in `x`.
      axis : int, optional
          The axis of `x` that is resampled.  Default is 0.
      window : array_like, callable, string, float, or tuple, optional
          Specifies the window applied to the signal in the Fourier
          domain.  See below for details.
      domain : string, optional
          A string indicating the domain of the input `x`:
  
          ``time``
             Consider the input `x` as time-domain. (Default)
          ``freq``
             Consider the input `x` as frequency-domain.
  
      Returns
      -------
      resampled_x or (resampled_x, resampled_t)
          Either the resampled array, or, if `t` was given, a tuple
          containing the resampled array and the corresponding resampled
          positions.
  
      See Also
      --------
      decimate : Downsample the signal after applying an FIR or IIR filter.
      resample_poly : Resample using polyphase filtering and an FIR filter.
  
      Notes
      -----
      The argument `window` controls a Fourier-domain window that tapers
      the Fourier spectrum before zero-padding to alleviate ringing in
      the resampled values for sampled signals you didn't intend to be
      interpreted as band-limited.
  
      If `window` is a function, then it is called with a vector of inputs
      indicating the frequency bins (i.e. fftfreq(x.shape[axis]) ).
  
      If `window` is an array of the same length as `x.shape[axis]` it is
      assumed to be the window to be applied directly in the Fourier
      domain (with dc and low-frequency first).
  
      For any other type of `window`, the function `cusignal.get_window`
      is called to generate the window.
  
      The first sample of the returned vector is the same as the first
      sample of the input vector.  The spacing between samples is changed
      from ``dx`` to ``dx * len(x) / num``.
  
      If `t` is not None, then it represents the old sample positions,
      and the new sample positions will be returned as well as the new
      samples.
  
      As noted, `resample` uses FFT transformations, which can be very
      slow if the number of input or output samples is large and prime;
      see `scipy.fftpack.fft`.
  
      Examples
      --------
      Note that the end of the resampled data rises to meet the first
      sample of the next cycle:
  
      >>> import cusignal
      >>> import cupy as cp
  
      >>> x = cp.linspace(0, 10, 20, endpoint=False)
      >>> y = cp.cos(-x**2/6.0)
      >>> f = cusignal.resample(y, 100)
      >>> xnew = cp.linspace(0, 10, 100, endpoint=False)
  
      >>> import matplotlib.pyplot as plt
      >>> plt.plot(cp.asnumpy(x), cp.asnumpy(y), 'go-', cp.asnumpy(xnew), \
                  cp.asnumpy(f), '.-', 10, cp.asnumpy(y[0]), 'ro')
      >>> plt.legend(['data', 'resampled'], loc='best')
      >>> plt.show()
      """
      x = cp.asarray(x)
      Nx = x.shape[axis]
  
      if domain == "time":
          X = cp.fft.fft(x, axis=axis)
      elif domain == "freq":
          X = x
      else:
          raise NotImplementedError("domain should be 'time' or 'freq'")
  
      if window is not None:
          if callable(window):
              W = window(cp.fft.fftfreq(Nx))
          elif isinstance(window, cp.ndarray):
              if window.shape != (Nx,):
                  raise ValueError(
                      "window must have the same length as data"
                  )
              W = window
          else:
              W = cp.fft.ifftshift(get_window(window, Nx))
          newshape = [1] * x.ndim
          newshape[axis] = len(W)
          W.shape = newshape
          X = X * cp.asarray(W, dtype=X.dtype)
  
      sl = [slice(None)] * x.ndim
      newshape = list(x.shape)
      newshape[axis] = num
      N = int(np.minimum(num, Nx))
      nyq = N // 2 + 1  # Slice index that includes Nyquist
      Y = cp.zeros(newshape, dtype=X.dtype)
      sl[axis] = slice(0, nyq)
      Y[tuple(sl)] = X[tuple(sl)]
      if N > 2:  # avoid empty slice
          sl[axis] = slice(nyq - N, None)
          Y[tuple(sl)] = X[tuple(sl)]
  
      # symmetrize nyquest freq bins if N is even
      if N % 2 == 0:
          if num < Nx:
              # select the component of Y at frequency +N/2,
              # add the component of X at -N/2
              sl[axis] = slice(-N // 2, -N // 2 + 1)
              Y[tuple(sl)] += X[tuple(sl)]
          elif Nx < num:
              # select the component at frequency +N/2 and halve it
              sl[axis] = slice(N // 2, N // 2 + 1)
              Y[tuple(sl)] *= 0.5
              temp = Y[tuple(sl)]
              # set the component at -N/2 equal to the component at +N/2
              sl[axis] = slice(num - N // 2, num - N // 2 + 1)
              Y[tuple(sl)] = temp
  
      y = cp.fft.ifft(Y, axis=axis) * (float(num) / float(Nx))
  
      if x.dtype.char not in ["F", "D"]:
          y = y.real
  
      if t is None:
          return y
      else:
          new_t = (
              cp.arange(0, num) * (t[1] - t[0]) * Nx / float(num) + t[0]
          )
          return y, new_t
  
file_path: cusignal/python/cusignal/filtering/resample.py
imports: from math import gcd
  import cupy
  import numpy
  from ..filter_design.fir_filter_design import firwin
  from ..windows.windows import get_window
  from ._upfirdn_cuda import _output_len, _UpFIRDn
label_desc: Use image node_id 3 for calling a global function with example usage: resample(x, num, t, axis, window, domain) and returns: y, y,  new_t