code: def compute_jvp_multi(tangent, jac):
      """Convenience function to compute the Jacobian-vector product for a given
      vector of gradient outputs and a Jacobian for a tape with multiple measurements.
  
      Args:
          tangent (tensor_like, list): tangent vector
          jac (tensor_like, tuple): Jacobian matrix
  
      Returns:
          tensor_like: the Jacobian-vector product
  
      **Examples**
  
      1. For a single parameter and multiple measurements (one without shape and one with shape, e.g. expval and probs):
  
      .. code-block:: pycon
  
          >>> tangent = np.array([2.0])
          >>> jac = tuple([np.array([0.3]), np.array([0.2, 0.5])])
          >>> qml.gradients.compute_jvp_multi(tangent, jac)
          (np.array([0.6]), np.array([0.4, 1. ]))
  
      2. For multiple parameters (in this case 2 parameters) and multiple measurements (one without shape and one with
      shape, e.g. expval and probs):
  
      .. code-block:: pycon
  
          >>> tangent = np.array([1.0, 2.0])
          >>> jac = tuple([tuple([np.array([0.3]), np.array([0.4])]), tuple([np.array([0.2, 0.5]), np.array([0.3, 0.8])]),])
          >>> qml.gradients.compute_jvp_multi(tangent, jac)
          (np.array([1.1]), np.array([0.8, 2.1]))
      """
      if jac is None:
          return None
      return tuple(compute_jvp_single(tangent, j) for j in jac)
  
imports: import numpy
  import pennylane
  from pennylane.measurements import ProbabilityMP
label_desc: Use image node_id 2 for calling a global function with example usage: compute_jvp_multi(tangent, jac) and returns: tuple, None