docs(mge/functional): fix F.svd docstring

GitOrigin-RevId: b84e5fdc46de51e68b93cf448af8a3e0d2e44c32

imperative/python/megengine/functional/math.py

@@ -656,19 +656,32 @@ def dot(inp1: Tensor, inp2: Tensor) -> Tensor:
 
 
 def svd(inp: Tensor, full_matrices=False, compute_uv=True) -> Tensor:
-    r"""Returns a singular value decomposition ``A = USVh`` of a matrix (or a stack of matrices) ``x`` , where ``U`` is a matrix (or a stack of matrices) with orthonormal columns, ``S`` is a vector of non-negative numbers (or stack of vectors), and ``Vh`` is a matrix (or a stack of matrices) with orthonormal rows.
+    r"""Computes the singular value decomposition of a matrix (or a stack of matrices) ``inp``.
+
+    Let :math:`X` be the input matrix (or a stack of input matrices), the output should satisfies:
+
+    .. math::
+        X = U * diag(S) * Vh
+
+    where ``U`` is a matrix (or stack of vectors) with orthonormal columns, ``S`` is a vector of 
+    non-negative numbers (or stack of vectors), and ``Vh`` is a matrix (or a stack of matrices) 
+    with orthonormal rows.
 
     Args:
-        x (Tensor): A input real tensor having the shape ``(..., M, N)`` with ``x.ndim >= 2`` .
-        full_matrices (bool, optional): If ``False`` , ``U`` and ``Vh`` have the shapes  ``(..., M, K)`` and ``(..., K, N)`` , respectively, where ``K = min(M, N)`` . If ``True`` , the shapes  are ``(..., M, M)`` and ``(..., N, N)`` , respectively. Default: ``False`` . 
+        inp (Tensor): A input real tensor having the shape ``(..., M, N)`` with ``inp.ndim >= 2`` .
+        full_matrices (bool, optional): If ``False`` , ``U`` and ``Vh`` have the shapes  ``(..., M, K)``
+            and ``(..., K, N)`` , respectively, where ``K = min(M, N)`` . If ``True`` , the shapes 
+            are ``(..., M, M)`` and ``(..., N, N)`` , respectively. Default: ``False`` . 
         compute_uv (bool, optional): Whether or not to compute ``U`` and ``Vh`` in addition to ``S`` . Default: ``True`` .
 
     Note:
         * naive does not support ``full_matrices`` and ``compute_uv`` as ``True`` .
 
     Returns:
-        Returns a tuple ( ``U`` , ``S`` , ``Vh`` ), which are  SVD factors ``U`` , ``S``, ``Vh`` of  input matrix ``x``. ( ``U`` , ``Vh`` only returned when ``compute_uv`` is True). 
-            ``U`` contains matrices orthonormal columns (i.e., the columns are left singular vectors). If ``full_matrices`` is ``True`` , the array must have shape ``(..., M, M)`` . If ``full_matrices`` is ``False`` , the array must have shape ``(..., M, K)`` , where ``K = min(M, N)`` .
+        Returns a tuple ( ``U`` , ``S`` , ``Vh`` ), which are SVD factors ``U`` , ``S``, ``Vh`` of  input matrix ``inp``. 
+        ( ``U`` , ``Vh`` only returned when ``compute_uv`` is True). ``U`` contains matrices orthonormal columns 
+        (i.e., the columns are left singular vectors). If ``full_matrices`` is ``True`` , the array must have shape 
+        ``(..., M, M)`` . If ``full_matrices`` is ``False`` , the array must have shape ``(..., M, K)`` , where ``K = min(M, N)`` .
 
     Examples:
         >>> import numpy as np