code: def _fftconv_faster(x, h, mode):
      """
      See if using fftconvolve or convolve is faster.
  
      Parameters
      ----------
      x : np.ndarray
          Signal
      h : np.ndarray
          Kernel
      mode : str
          Mode passed to convolve
  
      Returns
      -------
      fft_faster : bool
  
      Notes
      -----
      See docstring of `choose_conv_method` for details on tuning hardware.
  
      See pull request 11031 for more detail:
      https://github.com/scipy/scipy/pull/11031.
  
      """
      fft_ops, direct_ops = _conv_ops(x.shape, h.shape, mode)
      offset = -1e-3 if x.ndim == 1 else -1e-4
      constants = (
          {
              "valid": (1.89095737e-9, 2.1364985e-10, offset),
              "full": (1.7649070e-9, 2.1414831e-10, offset),
              "same": (3.2646654e-9, 2.8478277e-10, offset)
              if h.size <= x.size
              else (3.21635404e-9, 1.1773253e-8, -1e-5),
          }
          if x.ndim == 1
          else {
              "valid": (1.85927e-9, 2.11242e-8, offset),
              "full": (1.99817e-9, 1.66174e-8, offset),
              "same": (2.04735e-9, 1.55367e-8, offset),
          }
      )
      O_fft, O_direct, O_offset = constants[mode]
      return O_fft * fft_ops < O_direct * direct_ops + O_offset
  
imports: import operator
  import math
  import timeit
  from scipy.spatial import cKDTree
  from .None import sigtools, dlti
  from ._upfirdn import upfirdn, _output_len, _upfirdn_modes
  from scipy import linalg, fft
  from scipy.fft._helper import _init_nd_shape_and_axes
  from scipy._lib._util import prod
  import numpy
  from scipy.special import lambertw
  from .windows import get_window
  from ._arraytools import axis_slice, axis_reverse, odd_ext, even_ext, const_ext
  from .filter_design import cheby1, _validate_sos
  from .fir_filter_design import firwin
  from ._sosfilt import _sosfilt
  import warnings
label_desc: Use image node_id 15 for calling a global function with example usage: _fftconv_faster(x, h, mode) and returns: unknown