[DOC] Clarify the difference of paddle.norm and np.linalg.norm (#32530)

* [DOC] Clarify the difference between paddle.norm and np.linalg.norm

python/paddle/tensor/linalg.py

@@ -177,6 +177,12 @@ def norm(x, p='fro', axis=None, keepdim=False, name=None):
     Returns the matrix norm (Frobenius) or vector norm (the 1-norm, the Euclidean
     or 2-norm, and in general the p-norm for p > 0) of a given tensor.
 
+    .. note::
+        This norm API is different from `numpy.linalg.norm`.
+        This api supports high-order input tensors (rank >= 3), and certain axis need to be pointed out to calculate the norm.
+        But `numpy.linalg.norm` only supports 1-D vector or 2-D matrix as input tensor.
+        For p-order matrix norm, this api actually treats matrix as a flattened vector to calculate the vector norm, NOT REAL MATRIX NORM.
+
     Args:
         x (Tensor): The input tensor could be N-D tensor, and the input data
             type could be float32 or float64.
@@ -344,6 +350,10 @@ def norm(x, p='fro', axis=None, keepdim=False, name=None):
         return reduce_out
 
     def p_matrix_norm(input, porder=1., axis=axis, keepdim=False, name=None):
+        """
+        NOTE:
+            This function actually treats the matrix as flattened vector to calculate vector norm instead of matrix norm.
+        """
         block = LayerHelper('norm', **locals())
         out = block.create_variable_for_type_inference(
             dtype=block.input_dtype())