diff --git a/ivy/array/linear_algebra.py b/ivy/array/linear_algebra.py
index 8ca5b248a1c7f..624063290ca39 100644
--- a/ivy/array/linear_algebra.py
+++ b/ivy/array/linear_algebra.py
@@ -878,6 +878,82 @@ def vector_norm(
         dtype: Optional[Union[ivy.Dtype, ivy.NativeDtype]] = None,
         out: Optional[ivy.Array] = None,
     ) -> ivy.Array:
+        """
+            ivy.Array instance method variant of ivy.vector_norm.
+            This method computes the vector norm of a vector (or batch of vectors).
+
+        Parameters
+        ----------
+        self
+            Input array. Should have a floating-point data type.
+        axis
+            If an integer, ``axis`` specifies the axis (dimension) along which to
+            compute vector norms. If an n-tuple, ``axis`` specifies the axes
+            (dimensions) along which to compute batched vector norms. If ``None``,
+            the vector norm must be computed over all array values (i.e., equivalent
+            to computing the vector norm of a flattened array). Negative indices are
+            also supported. Default: ``None``.
+        keepdims
+            If ``True``, the axes (dimensions) specified by ``axis`` must be included
+            in the result as singleton dimensions, and, accordingly, the result must be
+            compatible with the input array (see :ref:`broadcasting`). Otherwise, if
+            ``False``, the axes (dimensions) specified by ``axis`` must not be included
+            in the result.
+            Default: ``False``.
+        ord
+            order of the norm. The following mathematical norms are supported:
+
+            +------------------+----------------------------+
+            | ord              | description                |
+            +==================+============================+
+            | 1                | L1-norm (Manhattan)        |
+            +------------------+----------------------------+
+            | 2                | L2-norm (Euclidean)        |
+            +------------------+----------------------------+
+            | inf              | infinity norm              |
+            +------------------+----------------------------+
+            | (int,float >= 1) | p-norm                     |
+            +------------------+----------------------------+
+
+            The following non-mathematical "norms" are also supported:
+
+            +------------------+--------------------------------+
+            | ord              | description                    |
+            +==================+================================+
+            | 0                | sum(a != 0)                    |
+            +------------------+--------------------------------+
+            | -inf             | min(abs(a))                    |
+            +------------------+--------------------------------+
+            | (int,float < 1)  | sum(abs(a)**ord)**(1./ord)     |
+            +------------------+--------------------------------+
+
+            Default: ``2``.
+        dtype
+            data type that may be used to perform the computation more precisely.
+            The input array ``self`` gets cast to ``dtype`` before the function's
+            computations.
+        out
+            optional output array, for writing the result to. It must have a shape
+            that the inputs broadcast to.
+
+        Returns
+        -------
+        ret
+            an array containing the vector norms. If ``axis`` is ``None``, the returned
+            array must be a zero-dimensional array containing a vector norm. If ``axis``
+            is a scalar value (``int`` or ``float``), the returned array must have a
+            rank which is one less than the rank of ``self``. If ``axis`` is a
+            ``n``-tuple, the returned array must have a rank which is ``n`` less than
+            the rank of ``self``. The returned array must have a floating-point data
+            type determined by :ref:`type-promotion`.
+
+        Examples
+        --------
+        >>> x = ivy.array([1., 2., 3.])
+        >>> y = x.vector_norm()
+        >>> print(y)
+        ivy.array([3.7416575])
+        """
         return ivy.vector_norm(
             self._data, axis=axis, keepdims=keepdims, ord=ord, dtype=dtype, out=out
         )
diff --git a/ivy/container/linear_algebra.py b/ivy/container/linear_algebra.py
index 1e2637f552eeb..68468b290da9f 100644
--- a/ivy/container/linear_algebra.py
+++ b/ivy/container/linear_algebra.py
@@ -2698,6 +2698,15 @@ def static_vector_norm(
             array must have a floating-point data type determined
             by :ref:`type-promotion`.
 
+        Examples
+        --------
+        >>> x = ivy.Container(a = [1., 2., 3.], b = [-2., 0., 3.2])
+        >>> y = ivy.Container.static_vector_norm(x)
+        >>> print(y)
+        {
+            a: ivy.array([3.7416575]),
+            b: ivy.array([3.77359247])
+        }
         """
         return ContainerBase.cont_multi_map_in_function(
             "vector_norm",
@@ -2802,6 +2811,15 @@ def vector_norm(
             ``x``. The returned array must have a floating-point data type
             determined by :ref:`type-promotion`.
 
+        Examples
+        --------
+        >>> x = ivy.Container(a = [1., 2., 3.], b = [-2., 0., 3.2])
+        >>> y = x.vector_norm()
+        >>> print(y)
+        {
+            a: ivy.array([3.7416575]),
+            b: ivy.array([3.77359247])
+        }
         """
         return self.static_vector_norm(
             self,
diff --git a/ivy/functional/ivy/linear_algebra.py b/ivy/functional/ivy/linear_algebra.py
index 7b5a6a5fbd3ec..d80125c8760e1 100644
--- a/ivy/functional/ivy/linear_algebra.py
+++ b/ivy/functional/ivy/linear_algebra.py
@@ -2448,6 +2448,53 @@ def vector_norm(
     but this function is *nestable*, and therefore also accepts :class:`ivy.Container`
     instances in place of any of the arguments.
 
+    Examples
+    --------
+    >>> x = ivy.array([1., 2., 3.])
+    >>> y = ivy.vector_norm(x)
+    >>> print(y)
+    ivy.array([3.7416575])
+
+    >>> x = ivy.array([[1, 2, 3], [1.3, 2.4, -1.2]])
+    >>> y = ivy.vector_norm(x, axis = 1, ord = 1, dtype = ivy.float32)
+    >>> print(y)
+    ivy.array([6., 4.9000001])
+
+    >>> x = ivy.array([[1, 2, 3], [1.3, 2.4, -1.2]])
+    >>> y = ivy.vector_norm(x, axis = 0, keepdims = True,  ord = float("inf"))
+    >>> print(y)
+
+    ivy.array([[1.3, 2.4, 3.]])
+
+    >>> x = ivy.native_array([1, 2, 3, 4], dtype = ivy.float32)
+    >>> y = ivy.vector_norm(x, ord = 3.)
+    >>> print(y)
+
+    ivy.array([4.64158917])
+
+    >>> x = ivy.array([1,2,3,4], dtype = ivy.float16)
+    >>> z = ivy.empty(shape = 1)
+    >>> print(z)
+    ivy.array([0.  , 2.  , 0.  , 2.25])
+    >>> y = ivy.vector_norm(x, ord = 0, out = z)
+    >>> print(z)
+    ivy.array([4.])
+    >>> print(y)
+    ivy.array([4.])
+
+    >>> x = ivy.arange(8).reshape((2,2,2))
+    >>> y = ivy.vector_norm(x, axis = (0,1), ord = float("-inf"))
+    >>> print(y)
+    ivy.array([2, 4])
+
+    >>> x = ivy.Container(a = [-1., 1., -2., 2.], b = [0., 1.2, 2.3, -3.1])
+    >>> y = ivy.vector_norm(x, ord = -1)
+    >>> print(y)
+    {
+        a: ivy.array([0.33333334]),
+        b: ivy.array([0.])
+    }
+
     """
     return current_backend(x).vector_norm(
         x, axis=axis, keepdims=keepdims, ord=ord, dtype=dtype, out=out