class_docstr: Class for an n-fold product manifold :math:`M^n`.
  
  Define a manifold as the product manifold of n copies of a given base
  manifold M.
  
  Parameters
  ----------
  space : NFoldManifold
      Base space.
  scales : array-like
      Scale of each metric in the product.
docstr: Compute the pointwise norms of a tangent vector.
  
  Compute the norms of the components of a tangent vector at the different
  sampling points of a base curve.
  
  Parameters
  ----------
  tangent_vec : array-like, shape=[..., n_copies, *base_shape]
      Tangent vector to discrete curve.
  base_point : array-like, shape=[..., n_copies, *base_shape]
      Point representing a discrete curve.
  
  Returns
  -------
  norm : array-like, shape=[..., *base_shape]
      Point-wise norms.
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code: def pointwise_norm(self, tangent_vec, base_point):
      """Compute the pointwise norms of a tangent vector.
  
      Compute the norms of the components of a tangent vector at the different
      sampling points of a base curve.
  
      Parameters
      ----------
      tangent_vec : array-like, shape=[..., n_copies, *base_shape]
          Tangent vector to discrete curve.
      base_point : array-like, shape=[..., n_copies, *base_shape]
          Point representing a discrete curve.
  
      Returns
      -------
      norm : array-like, shape=[..., *base_shape]
          Point-wise norms.
      """
      sq_norm = self.pointwise_inner_product(
          tangent_vec_a=tangent_vec,
          tangent_vec_b=tangent_vec,
          base_point=base_point,
      )
      return gs.sqrt(sq_norm)
  
imports: import geomstats.backend
  import geomstats.errors
  from geomstats.geometry.manifold import Manifold
  from geomstats.geometry.riemannian_metric import RiemannianMetric
  from geomstats.vectorization import get_batch_shape
label_desc: Use image node_id 5 for calling the NFoldMetric obj's underlying member method code with example usage: obj.pointwise_norm(tangent_vec, base_point) and returns: gs