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`, but can also be used when performing many
      convolutions of the same input shapes and dtypes, determining
      which method to use for all of them, either to avoid the overhead of the
      'auto' option or to use accurate real-world measurements.
  
      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
  
      Examples
      --------
      Estimate the fastest method for a given input:
  
      >>> import cusignal
      >>> import cupy as cp
      >>> a = cp.random.randn(1000)
      >>> b = cp.random.randn(1000000)
      >>> method = cusignal.choose_conv_method(a, b, mode='same')
      >>> method
      'fft'
  
      This can then be applied to other arrays of the same dtype and shape:
  
      >>> c = cp.random.randn(1000)
      >>> d = cp.random.randn(1000000)
      >>> # `method` works with correlate and convolve
      >>> corr1 = cusignal.correlate(a, b, mode='same', method=method)
      >>> corr2 = cusignal.correlate(c, d, mode='same', method=method)
      >>> conv1 = cusignal.convolve(a, b, mode='same', method=method)
      >>> conv2 = cusignal.convolve(c, d, mode='same', method=method)
  
      """
      volume = cp.asarray(in1)
      kernel = cp.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
  
      # fftconvolve doesn't support complex256
      fftconv_unsup = (
          "complex256" if sys.maxsize > 2**32 else "complex192"
      )
      if hasattr(cp, fftconv_unsup):
          if (
              volume.dtype == fftconv_unsup
              or kernel.dtype == fftconv_unsup
          ):
              return "direct"
  
      # 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(cp.abs(volume).max()) * int(
              cp.abs(kernel).max()
          )
          max_value *= int(min(volume.size, kernel.size))
          if max_value > 2 ** cp.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"
  
file_path: cusignal/python/cusignal/convolution/convolve.py
imports: import sys
  import cupy
  import numpy
  from ..utils.fftpack_helper import _init_nd_shape_and_axes_sorted, next_fast_len
  from .None import _convolution_cuda
  from .convolution_utils import _centered, _fftconv_faster, _inputs_swap_needed, _numeric_arrays, _timeit_fast
label_desc: Use image node_id 4 for calling a global function with example usage: choose_conv_method(in1, in2, mode, measure) and returns: str, chosen_method,  times, str, str, str, str