code: def vector_norm(data, axis=None, out=None):
      """Return length, i.e. Euclidean norm, of ndarray along axis.
  
      >>> v = numpy.random.random(3)
      >>> n = vector_norm(v)
      >>> numpy.allclose(n, numpy.linalg.norm(v))
      True
      >>> v = numpy.random.rand(6, 5, 3)
      >>> n = vector_norm(v, axis=-1)
      >>> numpy.allclose(n, numpy.sqrt(numpy.sum(v*v, axis=2)))
      True
      >>> n = vector_norm(v, axis=1)
      >>> numpy.allclose(n, numpy.sqrt(numpy.sum(v*v, axis=1)))
      True
      >>> v = numpy.random.rand(5, 4, 3)
      >>> n = numpy.empty((5, 3))
      >>> vector_norm(v, axis=1, out=n)
      >>> numpy.allclose(n, numpy.sqrt(numpy.sum(v*v, axis=1)))
      True
      >>> vector_norm([])
      0.0
      >>> vector_norm([1])
      1.0
  
      """
      data = numpy.array(data, dtype=numpy.float64, copy=True)
      if out is None:
          if data.ndim == 1:
              return math.sqrt(numpy.dot(data, data))
          data *= data
          out = numpy.atleast_1d(numpy.sum(data, axis=axis))
          numpy.sqrt(out, out)
          return out
      data *= data
      numpy.sum(data, axis=axis, out=out)
      numpy.sqrt(out, out)
      return None
  
imports: import math
  import numpy
label_desc: Use image node_id 38 for calling a global function with example usage: vector_norm(data, axis, out) and returns: None, out, math