code: def decimate(x, q, n=None, ftype="iir", axis=-1, zero_phase=True):
      """
      Downsample the signal after applying an anti-aliasing filter.
  
      By default, an order 8 Chebyshev type I filter is used. A 30 point FIR
      filter with Hamming window is used if `ftype` is 'fir'.
  
      Parameters
      ----------
      x : array_like
          The signal to be downsampled, as an N-dimensional array.
      q : int
          The downsampling factor. When using IIR downsampling, it is recommended
          to call `decimate` multiple times for downsampling factors higher than
          13.
      n : int, optional
          The order of the filter (1 less than the length for 'fir'). Defaults to
          8 for 'iir' and 20 times the downsampling factor for 'fir'.
      ftype : str {'iir', 'fir'} or ``dlti`` instance, optional
          If 'iir' or 'fir', specifies the type of lowpass filter. If an instance
          of an `dlti` object, uses that object to filter before downsampling.
      axis : int, optional
          The axis along which to decimate.
      zero_phase : bool, optional
          Prevent phase shift by filtering with `filtfilt` instead of `lfilter`
          when using an IIR filter, and 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.
  
      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.
      """
  
      x = cupy.asarray(x)
      q = operator.index(q)
  
      if n is not None:
          n = operator.index(n)
  
      result_type = x.dtype
      if (
          not cupy.issubdtype(result_type, cupy.inexact)
          or result_type.type == cupy.float16
      ):
          # upcast integers and float16 to float64
          result_type = cupy.float64
  
      if ftype == "fir":
          if n is None:
              half_len = (
                  10 * q
              )  # reasonable cutoff for our sinc-like function
              n = 2 * half_len
          b, a = firwin(n + 1, 1.0 / q, window="hamming"), 1.0
          b = cupy.asarray(b, dtype=result_type)
          a = cupy.asarray(a, dtype=result_type)
      elif ftype == "iir":
          iir_use_sos = True
          if n is None:
              n = 8
          sos = cheby1(n, 0.05, 0.8 / q, output="sos")
          sos = cupy.asarray(sos, dtype=result_type)
      elif isinstance(ftype, dlti):
          system = ftype._as_zpk()
          if system.poles.shape[0] == 0:
              # FIR
              system = ftype._as_tf()
              b, a = system.num, system.den
              ftype = "fir"
          elif (
              any(cupy.iscomplex(system.poles))
              or any(cupy.iscomplex(system.poles))
              or cupy.iscomplex(system.gain)
          ):
              # sosfilt & sosfiltfilt don't handle complex coeffs
              iir_use_sos = False
              system = ftype._as_tf()
              b, a = system.num, system.den
          else:
              iir_use_sos = True
              sos = zpk2sos(system.zeros, system.poles, system.gain)
              sos = cupy.asarray(sos, dtype=result_type)
      else:
          raise ValueError("invalid ftype")
  
      sl = [slice(None)] * x.ndim
  
      if ftype == "fir":
          b = b / a
          if zero_phase:
              y = resample_poly(x, 1, q, axis=axis, window=b)
          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, up=1, down=q, axis=axis)
              sl[axis] = slice(None, n_out, None)
  
      else:  # IIR case
          if zero_phase:
              if iir_use_sos:
                  y = sosfiltfilt(sos, x, axis=axis)
              else:
                  y = filtfilt(b, a, x, axis=axis)
          else:
              if iir_use_sos:
                  y = sosfilt(sos, x, axis=axis)
              else:
                  y = lfilter(b, a, x, axis=axis)
  
          sl[axis] = slice(None, None, q)
  
      return y[tuple(sl)]
  
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 2 for calling a global function with example usage: decimate(x, q, n, ftype, axis, zero_phase) and returns: y