code: def asym_dot(
      X: np.ndarray, Y: np.ndarray, eps: float = 1e-7
  ) -> Union[float, np.ndarray]:
      """
      Computes the influence of training instances `Y` to test instances `X`. This is an asymmetric kernel.
      (:math:`X^T Y/\\|Y\\|^2`). See the `paper <https://arxiv.org/abs/2102.05262>`_ for more details. Each of `X` and
      `Y` should have a leading batch dimension of size at least 1.
  
      Parameters
      ----------
      X
          Matrix of vectors.
      Y
          Matrix of vectors.
      eps
          Numerical stability.
  
      Returns
      -------
          Matrix of asymmetric dot product similarity values between the vector(s) in X and vectors in Y.
      """
  
      assert (
          len(X.shape) > 1 and len(Y.shape) > 1
      ), "The vectors `X` and `Y` should have a leading batch dimension."
      assert (
          X.shape[1] == Y.shape[1]
      ), "The second dimension of `X` needs to be the same as the dimension of `Y`."
      denominator = np.linalg.norm(Y, axis=1) ** 2
      return np.dot(X, Y.T) / (denominator + eps)[None, :]
  
imports: from typing import Union
  import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: asym_dot(X, Y, eps) and returns: unknown