code: def norm(x, ord=None, axis=None):
      """Norm of a cupy.scipy.spmatrix
  
      This function is able to return one of seven different sparse matrix norms,
      depending on the value of the ``ord`` parameter.
  
      Args:
          x (sparse matrix) : Input sparse matrix.
          ord (non-zero int, inf, -inf, 'fro', optional) : Order of the norm (see
              table under ``Notes``). inf means numpy's `inf` object.
          axis : (int, 2-tuple of ints, None, optional): If `axis` is an
              integer, it specifies the axis of `x` along which to
              compute the vector norms.  If `axis` is a 2-tuple, it specifies the
              axes that hold 2-D matrices, and the matrix norms of these matrices
              are computed.  If `axis` is None then either a vector norm
              (when `x` is 1-D) or a matrix norm (when `x` is 2-D) is returned.
      Returns:
          ndarray : 0-D or 1-D array or norm(s).
  
      .. seealso:: :func:`scipy.sparse.linalg.norm`
      """
  
      if not cupyx.scipy.sparse.issparse(x):
          raise TypeError(("input is not sparse. use cupy.linalg.norm"))
  
      # Check the default case first and handle it immediately.
      if axis is None and ord in (None, "fro", "f"):
          return _sparse_frobenius_norm(x)
  
      # Some norms require functions that are not implemented for all types.
      x = x.tocsr()
  
      if axis is None:
          axis = (0, 1)
      elif not isinstance(axis, tuple):
          msg = "'axis' must be None, an integer or a tuple of integers"
          try:
              int_axis = int(axis)
          except TypeError:
              raise TypeError(msg)
          if axis != int_axis:
              raise TypeError(msg)
          axis = (int_axis,)
  
      nd = 2
      if len(axis) == 2:
          row_axis, col_axis = axis
          if not (-nd <= row_axis < nd and -nd <= col_axis < nd):
              raise ValueError(
                  "Invalid axis %r for an array with shape %r"
                  % (axis, x.shape)
              )
          if row_axis % nd == col_axis % nd:
              raise ValueError("Duplicate axes given.")
          if ord == 2:
              raise NotImplementedError
              # return _multi_svd_norm(x, row_axis, col_axis, amax)
          elif ord == -2:
              raise NotImplementedError
              # return _multi_svd_norm(x, row_axis, col_axis, amin)
          elif ord == 1:
              return abs(x).sum(axis=row_axis).max()
          elif ord == numpy.inf:
              return abs(x).sum(axis=col_axis).max()
          elif ord == -1:
              return abs(x).sum(axis=row_axis).min()
          elif ord == -numpy.inf:
              return abs(x).sum(axis=col_axis).min()
          elif ord in (None, "f", "fro"):
              # The axis order does not matter for this norm.
              return _sparse_frobenius_norm(x)
          else:
              raise ValueError("Invalid norm order for matrices.")
      elif len(axis) == 1:
          (a,) = axis
          if not (-nd <= a < nd):
              raise ValueError(
                  "Invalid axis %r for an array with shape %r"
                  % (axis, x.shape)
              )
          if ord == numpy.inf:
              return abs(x).max(axis=a).A.ravel()
          elif ord == -numpy.inf:
              return abs(x).min(axis=a).A.ravel()
          elif ord == 0:
              # Zero norm
              return (
                  (x != 0)
                  .astype(numpy.float32)
                  .sum(axis=a)
                  .ravel()
                  .astype(numpy.int_)
              )
          elif ord == 1:
              # special case for speedup
              return abs(x).sum(axis=a).ravel()
          elif ord in (2, None):
              return cupy.sqrt(abs(x).power(2).sum(axis=a)).ravel()
          else:
              try:
                  ord + 1
              except TypeError:
                  raise ValueError("Invalid norm order for vectors.")
              return cupy.power(
                  abs(x).power(ord).sum(axis=a), 1 / ord
              ).ravel()
      else:
          raise ValueError("Improper number of dimensions to norm.")
  
imports: import numpy
  import cupy
  import cupyx.scipy.sparse
label_desc: Use image node_id 2 for calling a global function with example usage: norm(x, ord, axis) and returns: _sparse_frobenius_norm, abs, abs, abs, abs, unknown, abs, abs, abs, cupy, cupy, _sparse_frobenius_norm