code: def choose_conv_method(in1, in2, mode="full", measure=False):
      """
      Find the fastest convolution/correlation method.
  
      This primarily exists to be called during the ``method='auto'`` option in
      `convolve` and `correlate`. It can also be used to determine the value of
      ``method`` for many different convolutions of the same dtype/shape.
      In addition, it supports timing the convolution to adapt the value of
      ``method`` to a particular set of inputs and/or hardware.
  
      Parameters
      ----------
      in1 : array_like
          The first argument passed into the convolution function.
      in2 : array_like
          The second argument passed into the convolution function.
      mode : str {'full', 'valid', 'same'}, optional
          A string indicating the size of the output:
  
          ``full``
             The output is the full discrete linear convolution
             of the inputs. (Default)
          ``valid``
             The output consists only of those elements that do not
             rely on the zero-padding.
          ``same``
             The output is the same size as `in1`, centered
             with respect to the 'full' output.
      measure : bool, optional
          If True, run and time the convolution of `in1` and `in2` with both
          methods and return the fastest. If False (default), predict the fastest
          method using precomputed values.
  
      Returns
      -------
      method : str
          A string indicating which convolution method is fastest, either
          'direct' or 'fft'
      times : dict, optional
          A dictionary containing the times (in seconds) needed for each method.
          This value is only returned if ``measure=True``.
  
      See Also
      --------
      convolve
      correlate
  
      Notes
      -----
      Generally, this method is 99% accurate for 2D signals and 85% accurate
      for 1D signals for randomly chosen input sizes. For precision, use
      ``measure=True`` to find the fastest method by timing the convolution.
      This can be used to avoid the minimal overhead of finding the fastest
      ``method`` later, or to adapt the value of ``method`` to a particular set
      of inputs.
  
      Experiments were run on an Amazon EC2 r5a.2xlarge machine to test this
      function. These experiments measured the ratio between the time required
      when using ``method='auto'`` and the time required for the fastest method
      (i.e., ``ratio = time_auto / min(time_fft, time_direct)``). In these
      experiments, we found:
  
      * There is a 95% chance of this ratio being less than 1.5 for 1D signals
        and a 99% chance of being less than 2.5 for 2D signals.
      * The ratio was always less than 2.5/5 for 1D/2D signals respectively.
      * This function is most inaccurate for 1D convolutions that take between 1
        and 10 milliseconds with ``method='direct'``. A good proxy for this
        (at least in our experiments) is ``1e6 <= in1.size * in2.size <= 1e7``.
  
      The 2D results almost certainly generalize to 3D/4D/etc because the
      implementation is the same (the 1D implementation is different).
  
      All the numbers above are specific to the EC2 machine. However, we did find
      that this function generalizes fairly decently across hardware. The speed
      tests were of similar quality (and even slightly better) than the same
      tests performed on the machine to tune this function's numbers (a mid-2014
      15-inch MacBook Pro with 16GB RAM and a 2.5GHz Intel i7 processor).
  
      There are cases when `fftconvolve` supports the inputs but this function
      returns `direct` (e.g., to protect against floating point integer
      precision).
  
      .. versionadded:: 0.19
  
      Examples
      --------
      Estimate the fastest method for a given input:
  
      >>> from scipy import signal
      >>> rng = np.random.default_rng()
      >>> img = rng.random((32, 32))
      >>> filter = rng.random((8, 8))
      >>> method = signal.choose_conv_method(img, filter, mode='same')
      >>> method
      'fft'
  
      This can then be applied to other arrays of the same dtype and shape:
  
      >>> img2 = rng.random((32, 32))
      >>> filter2 = rng.random((8, 8))
      >>> corr2 = signal.correlate(img2, filter2, mode='same', method=method)
      >>> conv2 = signal.convolve(img2, filter2, mode='same', method=method)
  
      The output of this function (``method``) works with `correlate` and
      `convolve`.
  
      """
      volume = np.asarray(in1)
      kernel = np.asarray(in2)
  
      if measure:
          times = {}
          for method in ["fft", "direct"]:
              times[method] = _timeit_fast(
                  lambda: convolve(
                      volume, kernel, mode=mode, method=method
                  )
              )
  
          chosen_method = (
              "fft" if times["fft"] < times["direct"] else "direct"
          )
          return chosen_method, times
  
      # for integer input,
      # catch when more precision required than float provides (representing an
      # integer as float can lose precision in fftconvolve if larger than 2**52)
      if any(
          [_numeric_arrays([x], kinds="ui") for x in [volume, kernel]]
      ):
          max_value = int(np.abs(volume).max()) * int(
              np.abs(kernel).max()
          )
          max_value *= int(min(volume.size, kernel.size))
          if max_value > 2 ** np.finfo("float").nmant - 1:
              return "direct"
  
      if _numeric_arrays([volume, kernel], kinds="b"):
          return "direct"
  
      if _numeric_arrays([volume, kernel]):
          if _fftconv_faster(volume, kernel, mode):
              return "fft"
  
      return "direct"
  
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 19 for calling a global function with example usage: choose_conv_method(in1, in2, mode, measure) and returns: str, chosen_method,  times, str, str, str