code: def decimate(x, q, n=None, axis=-1, zero_phase=True, gpupath=True):
      """
      Downsample the signal after applying an anti-aliasing filter.
  
      Parameters
      ----------
      x : array_like
          The signal to be downsampled, as an N-dimensional array.
      q : int
          The downsampling factor.
      n : int or array_like, optional
          The order of the filter (1 less than the length for FIR) to calculate,
          or the FIR filter coefficients to employ. Defaults to calculating the
          coefficients for 20 times the downsampling factor.
      axis : int, optional
          The axis along which to decimate.
      zero_phase : bool, optional
          Prevent shifting the outputs back by the filter's
          group delay when using an FIR filter. The default value of ``True`` is
          recommended, since a phase shift is generally not desired.
      gpupath : bool, Optional
          Optional path for filter design. gpupath == False may be desirable if
          filter sizes are small.
  
      Returns
      -------
      y : ndarray
          The down-sampled signal.
  
      See Also
      --------
      resample : Resample up or down using the FFT method.
      resample_poly : Resample using polyphase filtering and an FIR filter.
  
      Notes
      -----
      Only FIR filter types are currently supported in cuSignal.
      """
  
      x = cp.asarray(x)
      if gpupath:
          pp = cp
      else:
          pp = np
  
      if isinstance(n, (list, pp.ndarray)):
          b = pp.asarray(n)
      else:
          if n is None:
              half_len = (
                  10 * q
              )  # reasonable cutoff for our sinc-like function
              n = 2 * half_len
  
          b = firwin(n + 1, 1.0 / q, window="hamming", gpupath=gpupath)
  
      sl = [slice(None)] * x.ndim
  
      if zero_phase:
          y = resample_poly(
              x, 1, q, axis=axis, window=b, gpupath=gpupath
          )
      else:
          # upfirdn is generally faster than lfilter by a factor equal to the
          # downsampling factor, since it only calculates the needed outputs
          n_out = x.shape[axis] // q + bool(x.shape[axis] % q)
          y = upfirdn(b, x, 1, q, axis)
          sl[axis] = slice(None, n_out, None)
  
      return y[tuple(sl)]
  
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 2 for calling a global function with example usage: decimate(x, q, n, axis, zero_phase, gpupath) and returns: y