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 cupy as cp
      >>> import cupyx.scipy.signal import resample
  
      >>> x = cupy.linspace(0, 10, 20, endpoint=False)
      >>> y = cupy.cos(-x**2/6.0)
      >>> f = resample(y, 100)
      >>> xnew = cupy.linspace(0, 10, 100, endpoint=False)
  
      >>> import matplotlib.pyplot as plt
      >>> plt.plot(cupy.asnumpy(x), cupy.asnumpy(y), 'go-', cupy.asnumpy(xnew), \
                  cupy.asnumpy(f), '.-', 10, cupy.asnumpy(y[0]), 'ro')
      >>> plt.legend(['data', 'resampled'], loc='best')
      >>> plt.show()
      """
      if domain not in ("time", "freq"):
          raise ValueError(
              "Acceptable domain flags are 'time' or"
              " 'freq', not domain={}".format(domain)
          )
  
      x = cupy.asarray(x)
      Nx = x.shape[axis]
  
      # Check if we can use faster real FFT
      real_input = cupy.isrealobj(x)
  
      if domain == "time":
          # Forward transform
          if real_input:
              X = rfft(x, axis=axis)
          else:  # Full complex FFT
              X = fft(x, axis=axis)
      else:  # domain == 'freq'
          X = x
  
      # Apply window to spectrum
      if window is not None:
          if callable(window):
              W = window(fftfreq(Nx))
          elif isinstance(window, cupy.ndarray):
              if window.shape != (Nx,):
                  raise ValueError(
                      "window must have the same length as data"
                  )
              W = window
          else:
              W = ifftshift(get_window(window, Nx))
  
          newshape_W = [1] * x.ndim
          newshape_W[axis] = X.shape[axis]
          if real_input:
              # Fold the window back on itself to mimic complex behavior
              W_real = W.copy()
              W_real[1:] += W_real[-1:0:-1]
              W_real[1:] *= 0.5
              X *= W_real[: newshape_W[axis]].reshape(newshape_W)
          else:
              X *= W.reshape(newshape_W)
  
      # Copy each half of the original spectrum to the output spectrum, either
      # truncating high frequences (downsampling) or zero-padding them
      # (upsampling)
  
      # Placeholder array for output spectrum
      newshape = list(x.shape)
      if real_input:
          newshape[axis] = num // 2 + 1
      else:
          newshape[axis] = num
      Y = cupy.zeros(newshape, X.dtype)
  
      # Copy positive frequency components (and Nyquist, if present)
      N = min(num, Nx)
      nyq = N // 2 + 1  # Slice index that includes Nyquist if present
      sl = [slice(None)] * x.ndim
      sl[axis] = slice(0, nyq)
      Y[tuple(sl)] = X[tuple(sl)]
      if not real_input:
          # Copy negative frequency components
          if (
              N > 2
          ):  # (slice expression doesn't collapse to empty array)
              sl[axis] = slice(nyq - N, None)
              Y[tuple(sl)] = X[tuple(sl)]
  
      # Split/join Nyquist component(s) if present
      # So far we have set Y[+N/2]=X[+N/2]
      if N % 2 == 0:
          if num < Nx:  # downsampling
              if real_input:
                  sl[axis] = slice(N // 2, N // 2 + 1)
                  Y[tuple(sl)] *= 2.0
              else:
                  # 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:  # upsampling
              # select the component at frequency +N/2 and halve it
              sl[axis] = slice(N // 2, N // 2 + 1)
              Y[tuple(sl)] *= 0.5
              if not real_input:
                  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
  
      # Inverse transform
      if real_input:
          y = irfft(Y, num, axis=axis)
      else:
          y = ifft(Y, axis=axis, overwrite_x=True)
  
      y *= float(num) / float(Nx)
  
      if t is None:
          return y
      else:
          new_t = (
              cupy.arange(0, num) * (t[1] - t[0]) * Nx / float(num)
              + t[0]
          )
          return y, new_t
  
file_path: cupy/cupyx/scipy/signal/_resample.py
imports: import operator
  from math import gcd
  import cupy
  from cupyx.scipy.fft import fft, rfft, fftfreq, ifft, irfft, ifftshift
  from cupyx.scipy.signal._iir_filter_design import cheby1
  from cupyx.scipy.signal._fir_filter_design import firwin
  from cupyx.scipy.signal._iir_filter_conversions import zpk2sos
  from cupyx.scipy.signal._ltisys import dlti
  from cupyx.scipy.signal._upfirdn import upfirdn, _output_len
  from cupyx.scipy.signal._signaltools import sosfiltfilt, filtfilt, sosfilt, lfilter
  from cupyx.scipy.signal.windows._windows import get_window
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