code: def _np_conv_ok(volume, kernel, mode):
      """
      See if numpy supports convolution of `volume` and `kernel` (i.e. both are
      1D ndarrays and of the appropriate shape).  Numpy's 'same' mode uses the
      size of the larger input, while Scipy's uses the size of the first input.
  
      Invalid mode strings will return False and be caught by the calling func.
      """
      if volume.ndim == kernel.ndim == 1:
          if mode in ("full", "valid"):
              return True
          elif mode == "same":
              return volume.size >= kernel.size
      else:
          return False
  
imports: from __future__ import division, print_function, absolute_import
  import operator
  import threading
  import sys
  import timeit
  from scipy.spatial import cKDTree
  from .None import sigtools, dlti
  from ._upfirdn import upfirdn, _output_len
  from scipy._lib.six import callable
  from scipy._lib._version import NumpyVersion
  from scipy import fftpack, linalg
  from scipy.fftpack.helper import _init_nd_shape_and_axes_sorted
  from numpy import allclose, angle, arange, argsort, array, asarray, atleast_1d, atleast_2d, cast, dot, exp, expand_dims, iscomplexobj, mean, ndarray, newaxis, ones, pi, poly, polyadd, polyder, polydiv, polymul, polysub, polyval, product, r_, ravel, real_if_close, reshape, roots, sort, take, transpose, unique, where, zeros, zeros_like
  import numpy
  import math
  from scipy.special import factorial
  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
label_desc: Use image node_id 11 for calling a global function with example usage: _np_conv_ok(volume, kernel, mode) and returns: False, True, unknown