code: def sigmoid_kernel(X, Y=None, gamma=1.0, coef0=0.0, queue=None):
      """
      Compute the sigmoid kernel between X and Y:
          K(x, y) = tanh(scale*dot(x, y^T) + shift)
      for each pair of rows x in X and y in Y.
  
      Parameters
      ----------
      X : ndarray of shape (n_samples_X, n_features)
      Y : ndarray of shape (n_samples_Y, n_features)
      scale : float, default=1.0
      shift : float, default=0.0
  
      Returns
      -------
      kernel_matrix : ndarray of shape (n_samples_X, n_samples_Y)
      """
  
      X, Y = _check_inputs(X, Y)
      return _compute_kernel(
          {"method": "dense", "scale": gamma, "shift": coef0},
          _backend.sigmoid_kernel,
          X,
          Y,
          queue,
      )
  
file_path: scikit-learn-intelex/onedal/primitives/kernel_functions.py
imports: import numpy
  from onedal import _backend
  from ..common._policy import _get_policy
  from ..datatypes import _convert_to_supported, from_table, to_table
  from ..utils import _check_array
label_desc: Use image node_id 6 for calling a global function with example usage: sigmoid_kernel(X, Y, gamma, coef0, queue) and returns: _compute_kernel