code: def norm(a, ord=None, axis=None, keepdims=False, check_finite=True):
      """
      Matrix or vector norm.
  
      This function is able to return one of eight different matrix norms,
      or one of an infinite number of vector norms (described below), depending
      on the value of the ``ord`` parameter. For tensors with rank different from
      1 or 2, only `ord=None` is supported.
  
      Parameters
      ----------
      a : array_like
          Input array. If `axis` is None, `a` must be 1-D or 2-D, unless `ord`
          is None. If both `axis` and `ord` are None, the 2-norm of
          ``a.ravel`` will be returned.
      ord : {int, inf, -inf, 'fro', 'nuc', None}, 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 `a` 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 `a`
          is 1-D) or a matrix norm (when `a` is 2-D) is returned.
      keepdims : bool, optional
          If this is set to True, the axes which are normed over are left in the
          result as dimensions with size one. With this option the result will
          broadcast correctly against the original `a`.
      check_finite : bool, optional
          Whether to check that the input matrix contains only finite numbers.
          Disabling may give a performance gain, but may result in problems
          (crashes, non-termination) if the inputs do contain infinities or NaNs.
  
      Returns
      -------
      n : float or ndarray
          Norm of the matrix or vector(s).
  
      Notes
      -----
      For values of ``ord <= 0``, the result is, strictly speaking, not a
      mathematical 'norm', but it may still be useful for various numerical
      purposes.
  
      The following norms can be calculated:
  
      =====  ============================  ==========================
      ord    norm for matrices             norm for vectors
      =====  ============================  ==========================
      None   Frobenius norm                2-norm
      'fro'  Frobenius norm                --
      'nuc'  nuclear norm                  --
      inf    max(sum(abs(a), axis=1))      max(abs(a))
      -inf   min(sum(abs(a), axis=1))      min(abs(a))
      0      --                            sum(a != 0)
      1      max(sum(abs(a), axis=0))      as below
      -1     min(sum(abs(a), axis=0))      as below
      2      2-norm (largest sing. value)  as below
      -2     smallest singular value       as below
      other  --                            sum(abs(a)**ord)**(1./ord)
      =====  ============================  ==========================
  
      The Frobenius norm is given by [1]_:
  
          :math:`||A||_F = [\\sum_{i,j} abs(a_{i,j})^2]^{1/2}`
  
      The nuclear norm is the sum of the singular values.
  
      Both the Frobenius and nuclear norm orders are only defined for
      matrices.
  
      References
      ----------
      .. [1] G. H. Golub and C. F. Van Loan, *Matrix Computations*,
             Baltimore, MD, Johns Hopkins University Press, 1985, pg. 15
  
      Examples
      --------
      >>> import numpy as np
      >>> from scipy.linalg import norm
      >>> a = np.arange(9) - 4.0
      >>> a
      array([-4., -3., -2., -1.,  0.,  1.,  2.,  3.,  4.])
      >>> b = a.reshape((3, 3))
      >>> b
      array([[-4., -3., -2.],
             [-1.,  0.,  1.],
             [ 2.,  3.,  4.]])
  
      >>> norm(a)
      7.745966692414834
      >>> norm(b)
      7.745966692414834
      >>> norm(b, 'fro')
      7.745966692414834
      >>> norm(a, np.inf)
      4
      >>> norm(b, np.inf)
      9
      >>> norm(a, -np.inf)
      0
      >>> norm(b, -np.inf)
      2
  
      >>> norm(a, 1)
      20
      >>> norm(b, 1)
      7
      >>> norm(a, -1)
      -4.6566128774142013e-010
      >>> norm(b, -1)
      6
      >>> norm(a, 2)
      7.745966692414834
      >>> norm(b, 2)
      7.3484692283495345
  
      >>> norm(a, -2)
      0
      >>> norm(b, -2)
      1.8570331885190563e-016
      >>> norm(a, 3)
      5.8480354764257312
      >>> norm(a, -3)
      0
  
      """
      # Differs from numpy only in non-finite handling and the use of blas.
      if check_finite:
          a = np.asarray_chkfinite(a)
      else:
          a = np.asarray(a)
  
      if (
          a.size
          and a.dtype.char in "fdFD"
          and axis is None
          and not keepdims
      ):
          if ord in (None, 2) and (a.ndim == 1):
              # use blas for fast and stable euclidean norm
              nrm2 = get_blas_funcs(
                  "nrm2", dtype=a.dtype, ilp64="preferred"
              )
              return nrm2(a)
  
          if a.ndim == 2:
              # Use lapack for a couple fast matrix norms.
              # For some reason the *lange frobenius norm is slow.
              lange_args = None
              # Make sure this works if the user uses the axis keywords
              # to apply the norm to the transpose.
              if ord == 1:
                  if np.isfortran(a):
                      lange_args = "1", a
                  elif np.isfortran(a.T):
                      lange_args = "i", a.T
              elif ord == np.inf:
                  if np.isfortran(a):
                      lange_args = "i", a
                  elif np.isfortran(a.T):
                      lange_args = "1", a.T
              if lange_args:
                  lange = get_lapack_funcs(
                      "lange", dtype=a.dtype, ilp64="preferred"
                  )
                  return lange(*lange_args)
  
      # fall back to numpy in every other case
      return np.linalg.norm(a, ord=ord, axis=axis, keepdims=keepdims)
  
file_path: scipy/scipy/linalg/_misc.py
imports: import numpy
  from numpy.linalg import LinAlgError
  from .blas import get_blas_funcs
  from .lapack import get_lapack_funcs
label_desc: Use image node_id 1 for calling a global function with example usage: norm(a, ord, axis, keepdims, check_finite) and returns: np, nrm2, lange