diff --git a/ivy/data_classes/array/experimental/manipulation.py b/ivy/data_classes/array/experimental/manipulation.py
index 013af82658b0a..6cdfeb97689f0 100644
--- a/ivy/data_classes/array/experimental/manipulation.py
+++ b/ivy/data_classes/array/experimental/manipulation.py
@@ -1134,6 +1134,62 @@ def take(
             self, indices, axis=axis, mode=mode, fill_value=fill_value, out=out
         )
 
+    def unflatten(
+        self: ivy.Array,
+        /,
+        shape: Union[Tuple[int], ivy.Array, ivy.NativeArray],
+        dim: Optional[int] = 0,
+        *,
+        out: Optional[ivy.Array] = None,
+    ) -> ivy.Array:
+        """ivy.Array instance method variant of ivy.unflatten. This method
+        simply wraps the function, and so the docstring for ivy.unflatten also
+        applies to this method with minimal changes.
+
+        Parameters
+        ----------
+        self
+            input array
+        shape
+            array indices. Must have an integer data type.
+        dim
+            axis over which to unflatten. If `axis` is negative,
+            the function must determine the axis along which to select values
+            by counting from the last dimension.
+            By default, the flattened input array is used.
+        out
+            optional output array, for writing the result to. It must
+            have a shape that the inputs broadcast to.
+
+        Returns
+        -------
+            ret
+                an array having the same data type as `x`.
+                The output array must have the same rank
+                (i.e., number of dimensions) as `x` and
+                must have the same shape as `x`,
+                except for the axis specified by `dim`
+                which is replaced with a tuple specified in `shape`.
+
+
+        Examples
+        --------
+        With 'ivy.Array' input:
+
+        >>> x = ivy.array([[1.2, 2.3, 3.4, 4.5],
+                           [5.6, 6.7, 7.8, 8.9]])
+        >>> shape = (2, 2)
+        >>> y = x.unflatten(shape=shape, dim=dim, out=y)
+        >>> print(y)
+        ivy.array([[[1.2, 2.3], [3.4, 4.5]], [[5.6, 6.7], [7.8, 8.9]]])
+        """
+        return ivy.unflatten(
+            self._data,
+            shape=shape,
+            dim=dim,
+            out=out,
+        )
+
     def trim_zeros(
         self: ivy.Array,
         /,
diff --git a/ivy/data_classes/container/experimental/manipulation.py b/ivy/data_classes/container/experimental/manipulation.py
index 20e72671746e1..fb293ce7ff254 100644
--- a/ivy/data_classes/container/experimental/manipulation.py
+++ b/ivy/data_classes/container/experimental/manipulation.py
@@ -4065,6 +4065,180 @@ def trim_zeros(
         """
         return self._static_trim_zeros(self, trim=trim)
 
+    @staticmethod
+    def _static_unflatten(
+        x: Union[int, ivy.Array, ivy.NativeArray, ivy.Container],
+        /,
+        shape: Union[Tuple[int], ivy.Array, ivy.NativeArray, ivy.Container],
+        dim: Optional[Union[int, ivy.Container]] = 0,
+        *,
+        out: Optional[Union[ivy.Array, ivy.Container]] = None,
+        key_chains: Optional[Union[List[str], Dict[str, str], ivy.Container]] = None,
+        to_apply: Union[bool, ivy.Container] = True,
+        prune_unapplied: Union[bool, ivy.Container] = False,
+        map_sequences: Union[bool, ivy.Container] = False,
+    ) -> ivy.Container:
+        """ivy.Container static method variant of ivy.unflatten. This method
+        simply wraps the function, and so the docstring for ivy.unflatten also
+        applies to this method with minimal changes.
+
+        Parameters
+        ----------
+        x
+            input array
+        shape
+            array indices. Must have an integer data type.
+        dim
+            axis over which to select values. If `axis` is negative,
+            the function must determine the axis along which to select values
+            by counting from the last dimension.
+            By default, the flattened input array is used.
+        out
+            optional output array, for writing the result to. It must
+            have a shape that the inputs broadcast to.
+        key_chains
+            The key-chains to apply or not apply the method to.
+            Default is ``None``.
+        to_apply
+            If True, the method will be applied to key_chains,
+            otherwise key_chains will be skipped. Default is ``True``.
+        prune_unapplied
+            Whether to prune key_chains for which the function was
+            not applied. Default is ``False``.
+        map_sequences
+            Whether to also map method to sequences (lists, tuples).
+            Default is ``False``.
+
+        Returns
+        -------
+            ret
+                an array having the same data type as `x`.
+                The output array must have the same rank
+                (i.e., number of dimensions) as `x` and
+                must have the same shape as `x`,
+                except for the axis specified by `axis`
+                whose size must equal the number of elements in `indices`.
+
+
+        Examples
+        --------
+        With 'ivy.Container' input:
+
+        >>> x = ivy.Container(a = ivy.array([[True, False, False, True],
+                                            [False, True, False, True]])),
+        ...                     b = ivy.array([[1.2, 2.3, 3.4, 4.5],
+                                            [5.6, 6.7, 7.8, 8.9]]),
+        ...                     c = ivy.array([[1, 2, 3, 4],
+                                            [5, 6, 7, 8]]))
+        >>> dim = 1
+        >>> shape = (2, 2)
+        >>> y = ivy.Container._static_unflatten(x, shape=shape, dim=dim)
+        >>> print(y)
+        {
+            a: ivy.array([[[True, False], [False, True]],
+                        [[False, True], [False, True]]])
+            b: ivy.array([[[1.2, 2.3], [3.4, 4.5]], [[5.6, 6.7], [7.8, 8.9]]])
+            c: ivy.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
+        }
+        """
+        return ContainerBase.cont_multi_map_in_function(
+            "unflatten",
+            x,
+            shape=shape,
+            dim=dim,
+            out=out,
+            key_chains=key_chains,
+            to_apply=to_apply,
+            prune_unapplied=prune_unapplied,
+            map_sequences=map_sequences,
+        )
+
+    def unflatten(
+        self: ivy.Container,
+        /,
+        shape: Union[Tuple[int], ivy.Array, ivy.NativeArray, ivy.Container],
+        dim: Optional[Union[int, ivy.Container]] = 0,
+        *,
+        out: Optional[Union[ivy.Array, ivy.Container]] = None,
+        key_chains: Optional[Union[List[str], Dict[str, str], ivy.Container]] = None,
+        to_apply: Union[bool, ivy.Container] = True,
+        prune_unapplied: Union[bool, ivy.Container] = False,
+        map_sequences: Union[bool, ivy.Container] = False,
+    ) -> ivy.Container:
+        """ivy.Container instance method variant of ivy.unflatten. This method
+        simply wraps the function, and so the docstring for ivy.unflatten also
+        applies to this method with minimal changes.
+
+        Parameters
+        ----------
+        self
+            input array
+        shape
+            array indices. Must have an integer data type.
+        dim
+            axis over which to unflatten. If `axis` is negative,
+            the function must determine the axis along which to select values
+            by counting from the last dimension.
+            By default, the flattened input array is used.
+        out
+            optional output array, for writing the result to. It must
+            have a shape that the inputs broadcast to.
+        key_chains
+            The key-chains to apply or not apply the method to.
+            Default is ``None``.
+        to_apply
+            If True, the method will be applied to key_chains,
+            otherwise key_chains will be skipped. Default is ``True``.
+        prune_unapplied
+            Whether to prune key_chains for which the function was
+            not applied. Default is ``False``.
+        map_sequences
+            Whether to also map method to sequences (lists, tuples).
+            Default is ``False``.
+
+        Returns
+        -------
+            ret
+                an array having the same data type as `x`.
+                The output array must have the same rank
+                (i.e., number of dimensions) as `x` and
+                must have the same shape as `x`,
+                except for the axis specified by `dim`
+                which is replaced with a tuple specified in `shape`.
+
+
+        Examples
+        --------
+        With 'ivy.Container' input:
+
+        >>> x = ivy.Container(a = ivy.array([[True, False, False, True],
+                                            [False, True, False, True]])),
+        ...                     b = ivy.array([[1.2, 2.3, 3.4, 4.5],
+                                            [5.6, 6.7, 7.8, 8.9]]),
+        ...                     c = ivy.array([[1, 2, 3, 4],
+                                            [5, 6, 7, 8]]))
+        >>> dim = 1
+        >>> shape = (2, 2)
+        >>> y = x.unflatten(shape=shape, dim=dim)
+        >>> print(y)
+        {
+            a: ivy.array([[[True, False], [False, True]],
+                          [[False, True], [False, True]]])
+            b: ivy.array([[[1.2, 2.3], [3.4, 4.5]], [[5.6, 6.7], [7.8, 8.9]]])
+            c: ivy.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
+        }
+        """
+        return self._static_unflatten(
+            self,
+            shape=shape,
+            dim=dim,
+            out=out,
+            key_chains=key_chains,
+            to_apply=to_apply,
+            prune_unapplied=prune_unapplied,
+            map_sequences=map_sequences,
+        )
+
 
 def concat_from_sequence(
     self: ivy.Container,
@@ -4130,11 +4304,11 @@ def concat_from_sequence(
     >>> print(z)
     {
     'a': ivy.array([[[0, 1],
-                     [3, 2]],
-                     [[2, 3],
-                     [1, 0]]]),
+                    [3, 2]],
+                    [[2, 3],
+                    [1, 0]]]),
     'b': ivy.array([[[4, 5],
-                     [1, 0]]])
+                    [1, 0]]])
     }
     """
     new_input_sequence = (
diff --git a/ivy/functional/backends/jax/experimental/manipulation.py b/ivy/functional/backends/jax/experimental/manipulation.py
index 36759c9dd1dfd..59ee341ab7870 100644
--- a/ivy/functional/backends/jax/experimental/manipulation.py
+++ b/ivy/functional/backends/jax/experimental/manipulation.py
@@ -475,8 +475,8 @@ def trim_zeros(a: JaxArray, /, *, trim: Optional[str] = "bf") -> JaxArray:
 def unflatten(
     x: JaxArray,
     /,
-    dim: int = 0,
     shape: Tuple[int] = None,
+    dim: int = 0,
     *,
     out: Optional[JaxArray] = None,
     order: Optional[str] = None,
diff --git a/ivy/functional/backends/numpy/experimental/manipulation.py b/ivy/functional/backends/numpy/experimental/manipulation.py
index a4a7c9ed12756..17b57a75f0f33 100644
--- a/ivy/functional/backends/numpy/experimental/manipulation.py
+++ b/ivy/functional/backends/numpy/experimental/manipulation.py
@@ -607,8 +607,8 @@ def put_along_axis(
 def unflatten(
     x: np.ndarray,
     /,
-    dim: int = 0,
     shape: Tuple[int] = None,
+    dim: Optional[int] = 0,
     *,
     out: Optional[np.ndarray] = None,
     order: Optional[str] = None,
diff --git a/ivy/functional/backends/paddle/experimental/manipulation.py b/ivy/functional/backends/paddle/experimental/manipulation.py
index 788a73d263c76..c6a482ee60b2d 100644
--- a/ivy/functional/backends/paddle/experimental/manipulation.py
+++ b/ivy/functional/backends/paddle/experimental/manipulation.py
@@ -908,12 +908,26 @@ def put_along_axis(
 ]
 
 
+@with_supported_dtypes(
+    {
+        "2.6.0 and below": (
+            "int32",
+            "int64",
+            "float64",
+            "complex128",
+            "float32",
+            "complex64",
+            "bool",
+        )
+    },
+    backend_version,
+)
 @handle_out_argument
 def unflatten(
     x: paddle.Tensor,
     /,
-    dim: int = 0,
     shape: Tuple[int] = None,
+    dim: int = 0,
     *,
     out: Optional[paddle.Tensor] = None,
 ) -> paddle.Tensor:
diff --git a/ivy/functional/backends/tensorflow/experimental/manipulation.py b/ivy/functional/backends/tensorflow/experimental/manipulation.py
index e03aebe12f749..e188f00a8aaae 100644
--- a/ivy/functional/backends/tensorflow/experimental/manipulation.py
+++ b/ivy/functional/backends/tensorflow/experimental/manipulation.py
@@ -571,8 +571,8 @@ def trim_zeros(a: tf.Tensor, /, *, trim: Optional[str] = "bf") -> tf.Tensor:
 def unflatten(
     x: tf.Tensor,
     /,
-    dim: int = 0,
     shape: Tuple[int] = None,
+    dim: Optional[int] = 0,
     *,
     out: Optional[tf.Tensor] = None,
     name: Optional[str] = None,
diff --git a/ivy/functional/backends/torch/experimental/manipulation.py b/ivy/functional/backends/torch/experimental/manipulation.py
index 485884c6511ed..97dd7183fa442 100644
--- a/ivy/functional/backends/torch/experimental/manipulation.py
+++ b/ivy/functional/backends/torch/experimental/manipulation.py
@@ -649,8 +649,8 @@ def trim_zeros(a: torch.Tensor, /, *, trim: Optional[str] = "bf") -> torch.Tenso
 def unflatten(
     x: torch.Tensor,
     /,
-    dim: int = 0,
     shape: Tuple[int] = None,
+    dim: Optional[int] = 0,
     *,
     out: Optional[torch.Tensor] = None,
 ) -> torch.Tensor:
diff --git a/ivy/functional/ivy/experimental/manipulation.py b/ivy/functional/ivy/experimental/manipulation.py
index a4bf5fd435a47..d46b2bed23bc7 100644
--- a/ivy/functional/ivy/experimental/manipulation.py
+++ b/ivy/functional/ivy/experimental/manipulation.py
@@ -2884,9 +2884,9 @@ def trim_zeros(
 def unflatten(
     x: Union[ivy.Array, ivy.NativeArray],
     /,
-    *,
     dim: int,
     shape: Tuple[int],
+    *,
     out: Optional[ivy.Array] = None,
 ) -> ivy.Array:
     """Expand a dimension of the input tensor over multiple dimensions.
@@ -2930,4 +2930,4 @@ def unflatten(
     >>> ivy.unflatten(torch.randn(5, 12, 3), dim=-2, shape=(2, 2, 3, 1, 1)).shape
     torch.Size([5, 2, 2, 3, 1, 1, 3])
     """
-    return current_backend(x).unflatten(x, dim=dim, shape=shape, out=out)
+    return ivy.current_backend(x).unflatten(x, dim=dim, shape=shape, out=out)
diff --git a/ivy_tests/test_ivy/test_frontends/test_torch/test_miscellaneous_ops.py b/ivy_tests/test_ivy/test_frontends/test_torch/test_miscellaneous_ops.py
index 5a77b6cfbbe6e..5b15e0ec81308 100644
--- a/ivy_tests/test_ivy/test_frontends/test_torch/test_miscellaneous_ops.py
+++ b/ivy_tests/test_ivy/test_frontends/test_torch/test_miscellaneous_ops.py
@@ -1788,8 +1788,8 @@ def test_torch_triu_indices(
     ),
     get_axis=helpers.get_axis(
         shape=st.shared(helpers.get_shape(min_num_dims=1), key="shape"),
-        max_size=1,
-        min_size=1,
+        max_size=0,
+        min_size=0,
         force_int=True,
     ),
 )
@@ -1804,10 +1804,9 @@ def test_torch_unflatten(
     shape,
     get_axis,
 ):
-    if type(get_axis) is not tuple:
-        axis = get_axis
-    else:
-        axis = 0 if get_axis is None else get_axis[0]
+    axis = get_axis
+    if type(axis) is tuple:
+        axis = 0 if not get_axis else get_axis[0]
     dtype, x = dtype_and_values
 
     def factorization(n):
@@ -1835,7 +1834,8 @@ def get_factor(n):
             next = get_factor(n)
             factors.append(next)
             n //= next
-
+        if len(factors) > 1:
+            factors.remove(1)
         return factors
 
     shape_ = (
diff --git a/ivy_tests/test_ivy/test_functional/test_experimental/test_core/test_manipulation.py b/ivy_tests/test_ivy/test_functional/test_experimental/test_core/test_manipulation.py
index 87b09dccecfd6..d88849c914204 100644
--- a/ivy_tests/test_ivy/test_functional/test_experimental/test_core/test_manipulation.py
+++ b/ivy_tests/test_ivy/test_functional/test_experimental/test_core/test_manipulation.py
@@ -2,6 +2,7 @@
 from hypothesis import strategies as st, assume
 import hypothesis.extra.numpy as nph
 import numpy as np
+import math
 
 # local
 import ivy
@@ -1470,6 +1471,85 @@ def test_trim_zeros(
     )
 
 
+# unflatten
+@handle_test(
+    fn_tree="functional.ivy.experimental.unflatten",
+    shape=st.shared(helpers.get_shape(min_num_dims=1), key="shape"),
+    dtype_and_values=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("valid"),
+        min_num_dims=1,
+        shape_key="shape",
+    ),
+    get_axis=helpers.get_axis(
+        shape=st.shared(helpers.get_shape(min_num_dims=1), key="shape"),
+        max_size=0,
+        min_size=0,
+        force_int=True,
+    ),
+)
+def test_unflatten(
+    *,
+    dtype_and_values,
+    on_device,
+    fn_name,
+    test_flags,
+    backend_fw,
+    shape,
+    get_axis,
+):
+    axis = get_axis
+    if type(axis) is tuple:
+        axis = 0 if not get_axis else get_axis[0]
+    dtype, x = dtype_and_values
+
+    def factorization(n):
+        factors = [1]
+
+        def get_factor(n):
+            x_fixed = 2
+            cycle_size = 2
+            x = 2
+            factor = 1 if n % 2 else 2
+
+            while factor == 1:
+                for count in range(cycle_size):
+                    if factor > 1:
+                        break
+                    x = (x * x + 1) % n
+                    factor = math.gcd(x - x_fixed, n)
+
+                cycle_size *= 2
+                x_fixed = x
+
+            return factor
+
+        while n > 1:
+            next = get_factor(n)
+            factors.append(next)
+            n //= next
+
+        if len(factors) > 1:
+            factors.remove(1)
+        return factors
+
+    shape_ = (
+        tuple(factorization(shape[axis]))
+        if tuple(factorization(shape[axis]))
+        else shape
+    )
+    helpers.test_function(
+        input_dtypes=dtype,
+        backend_to_test=backend_fw,
+        test_flags=test_flags,
+        fn_name=fn_name,
+        on_device=on_device,
+        test_values=False,
+        x=x[0],
+        shape=shape_,
+        dim=axis,
+    )
+
+
 @handle_test(
     fn_tree="functional.ivy.experimental.unfold",
     dtype_values_axis=helpers.dtype_values_axis(