internlm/SWE-Fixer-Train-110K huggingface dataset:
Repository: ivy-llc/ivy

Issue:
<insert_into_nest_at_index>



Discussion:

Resolving patch:
diff --git a/ivy/functional/ivy/nest.py b/ivy/functional/ivy/nest.py
--- a/ivy/functional/ivy/nest.py
+++ b/ivy/functional/ivy/nest.py
@@ -217,7 +217,35 @@ def set_nest_at_index(
 
 
 @handle_exceptions
-def insert_into_nest_at_index(nest: Iterable, index: Tuple, value, /) -> None:
+def insert_into_nest_at_index(nest: Iterable, index: Tuple, value) -> None:
+    """
+    Recursively inserts a value into a nested data structure at a specified index.
+
+    This function traverses a nested data structure and inserts the provided `value`
+    at the specified `index`.
+
+    Parameters
+    ----------
+    nest : Iterable
+        The nested data structure.
+    index : Tuple
+        The index specifying the location where the `value` should be inserted.
+    value : object
+        The value to be inserted.
+
+    Returns
+    -------
+    None
+
+    Examples
+    --------
+    >>> nest = [[1, 2], [3, 4]]
+    >>> index = (1, 1)
+    >>> value = 99
+    >>> insert_into_nest_at_index(nest, index, value)
+    >>> print(nest)
+    [[1, 2], [3, 99, 4]]
+    """
     if len(index) == 1:
         idx = index[0]
         if isinstance(nest, list):


Test patch:
Repository: ivy-llc/ivy

Issue:
<_compute_cost_and_update_grads>
Meta.py


Discussion:

Resolving patch:
diff --git a/ivy/functional/ivy/meta.py b/ivy/functional/ivy/meta.py
--- a/ivy/functional/ivy/meta.py
+++ b/ivy/functional/ivy/meta.py
@@ -24,13 +24,56 @@ def _compute_cost_and_update_grads(
     all_grads,
     unique_outer,
     batched,
-    num_tasks,
+    num_tasks
 ):
+    """
+    Compute cost and update gradients.
+
+    This function computes the cost and updates gradients for optimization.
+
+    Parameters
+    ----------
+    cost_fn : function
+        The cost function.
+    order : int
+        The order of computation.
+    batch : object
+        The batch data.
+    variables : ivy.Container
+        The variables for optimization.
+    outer_v : object
+        Outer variable.
+    keep_outer_v : bool
+        Whether to keep outer variable.
+    average_across_steps_or_final : bool
+        Whether to average across steps or final.
+    all_grads : list
+        List to accumulate gradients.
+    unique_outer : bool
+        Whether outer variables are unique.
+    batched : bool
+        Whether the data is batched.
+    num_tasks : int
+        Number of tasks.
+
+    Returns
+    -------
+    object
+        The computed cost.
+
+    Examples
+    --------
+    >>> # Example usage here
+    >>> pass
+    """
     if order == 1:
-        cost, inner_grads = ivy.execute_with_gradients(
-            lambda v: cost_fn(
+        def cost_fn_with_variable(v):
+            return cost_fn(
                 batch, v=variables.cont_set_at_key_chains(v) if unique_outer else v
-            ),
+            )
+        
+        cost, inner_grads = ivy.execute_with_gradients(
+            cost_fn_with_variable,
             (
                 variables.cont_at_key_chains(outer_v, ignore_none=True)
                 if keep_outer_v
@@ -38,23 +81,28 @@ def _compute_cost_and_update_grads(
             ),
             retain_grads=False,
         )
+        
         var = (
             variables.cont_at_key_chains(outer_v, ignore_none=True)
             if keep_outer_v
             else variables.cont_prune_key_chains(outer_v, ignore_none=True)
         )
+        
         inner_grads = ivy.Container(
             {
                 k: ivy.zeros_like(v) if k not in inner_grads else inner_grads[k]
                 for k, v in var.cont_to_iterator()
             }
         )
+        
         if batched:
             inner_grads = ivy.multiply(inner_grads, num_tasks)
+        
         if average_across_steps_or_final:
             all_grads.append(inner_grads)
     else:
         cost = cost_fn(batch, v=variables)
+    
     return cost
 
 


Test patch:
Repository: ivy-llc/ivy

Issue:
min



Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/paddle/tensor/math.py b/ivy/functional/frontends/paddle/tensor/math.py
--- a/ivy/functional/frontends/paddle/tensor/math.py
+++ b/ivy/functional/frontends/paddle/tensor/math.py
@@ -57,6 +57,14 @@ def amax(x, axis=None, keepdims=False):
     return ivy.max(x, axis=axis, keepdims=keepdims)
 
 
+@with_supported_dtypes(
+    {"2.5.1 and below": ("float32", "float64", "int32", "int64")}, "paddle"
+)
+@to_ivy_arrays_and_back
+def amin(x, axis=None, keepdim=False, name=None):
+    return ivy.min(x, axis=axis, keepdims=keepdim)
+
+
 @with_supported_dtypes(
     {"2.5.1 and below": ("complex64", "complex128", "float32", "float64")},
     "paddle",
@@ -314,6 +322,14 @@ def maximum(x, y, name=None):
     return ivy.maximum(x, y)
 
 
+@with_supported_dtypes(
+    {"2.5.1 and below": ("float32", "float64", "int32", "int64")}, "paddle"
+)
+@to_ivy_arrays_and_back
+def min(x, axis=None, keepdim=False, name=None):
+    return ivy.min(x, axis=axis, keepdims=keepdim)
+
+
 @with_supported_dtypes(
     {"2.5.1 and below": ("float32", "float64", "int32", "int64")}, "paddle"
 )
@@ -502,10 +518,3 @@ def tanh(x, name=None):
 @to_ivy_arrays_and_back
 def trunc(x, name=None):
     return ivy.trunc(x)
-
-@with_supported_dtypes(
-    {"2.5.1 and below": ("float32", "float64", "int32", "int64")}, "paddle"
-)
-@to_ivy_arrays_and_back
-def amin(x, axis=None, keepdim=False, name=None):    
-    return ivy.min(x, axis=axis, keepdims=keepdim)


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_math.py b/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_math.py
--- a/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_math.py
+++ b/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_math.py
@@ -2139,6 +2139,40 @@ def test_paddle_stanh(
     )
 
 
+# min
+@handle_frontend_test(
+    fn_tree="paddle.tensor.math.min",
+    dtype_and_x=helpers.dtype_values_axis(
+        available_dtypes=helpers.get_dtypes("valid"),
+        min_axis=-1,
+        max_axis=0,
+        min_num_dims=1,
+        force_int_axis=False,
+    ),
+)
+def test_paddle_min(
+    *,
+    dtype_and_x,
+    on_device,
+    fn_tree,
+    frontend,
+    backend_fw,
+    test_flags,
+):
+    input_dtype, x, axis = dtype_and_x
+    helpers.test_frontend_function(
+        input_dtypes=input_dtype,
+        frontend=frontend,
+        backend_to_test=backend_fw,
+        test_flags=test_flags,
+        fn_tree=fn_tree,
+        on_device=on_device,
+        x=x[0],
+        axis=axis,
+        keepdim=False,
+    )
+
+
 # amin
 @handle_frontend_test(
     fn_tree="paddle.tensor.math.amin",
@@ -2167,6 +2201,6 @@ def test_paddle_amin(
         test_flags=test_flags,
         on_device=on_device,
         x=x[0],
-        axis = axis,
-        keepdim = keepdim,
+        axis=axis,
+        keepdim=keepdim,
     )
Repository: ivy-llc/ivy

Issue:
remainder



Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/paddle/tensor/tensor.py b/ivy/functional/frontends/paddle/tensor/tensor.py
--- a/ivy/functional/frontends/paddle/tensor/tensor.py
+++ b/ivy/functional/frontends/paddle/tensor/tensor.py
@@ -654,3 +654,7 @@ def std(self, axis=None, unbiased=True, keepdim=False, name=None):
     )
     def trunc(self, name=None):
         return paddle_frontend.Tensor(ivy.trunc(self._ivy_array))
+
+    @with_unsupported_dtypes({"2.5.1 and below": ("float16", "bfloat16")}, "paddle")
+    def remainder(self, y, name=None):
+        return ivy.remainder(self._ivy_array, y)


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_tensor.py b/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_tensor.py
--- a/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_tensor.py
+++ b/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_tensor.py
@@ -3020,3 +3020,42 @@ def test_torch_tensor_sqrt_(
         frontend=frontend,
         on_device=on_device,
     )
+
+
+# remainder
+@handle_frontend_method(
+    class_tree=CLASS_TREE,
+    init_tree="paddle.to_tensor",
+    method_name="remainder",
+    dtype_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("float"),
+        num_arrays=2,
+        allow_inf=False,
+        large_abs_safety_factor=2,
+        small_abs_safety_factor=2,
+        safety_factor_scale="log",
+        shared_dtype=True,
+    ),
+)
+def test_paddle_tensor_remainder(
+    dtype_and_x,
+    frontend_method_data,
+    init_flags,
+    method_flags,
+    frontend,
+    on_device,
+    backend_fw,
+):
+    input_dtype, x = dtype_and_x
+    helpers.test_frontend_method(
+        init_input_dtypes=input_dtype,
+        backend_to_test=backend_fw,
+        init_all_as_kwargs_np={"data": x[0]},
+        method_input_dtypes=input_dtype,
+        method_all_as_kwargs_np={"y": x[1]},
+        frontend_method_data=frontend_method_data,
+        init_flags=init_flags,
+        method_flags=method_flags,
+        frontend=frontend,
+        on_device=on_device,
+    )
Repository: ivy-llc/ivy

Issue:
<_train_tasks_batched>



Discussion:

Resolving patch:
diff --git a/ivy/functional/ivy/meta.py b/ivy/functional/ivy/meta.py
--- a/ivy/functional/ivy/meta.py
+++ b/ivy/functional/ivy/meta.py
@@ -214,6 +214,60 @@ def _train_tasks_batched(
     num_tasks,
     stop_gradients,
 ):
+    """
+    Train tasks in a batched manner.
+
+    This function trains tasks in a batched manner with optional inner and outer batch functions.
+
+    Parameters
+    ----------
+    batch : object
+        The batch data.
+    inner_batch_fn : function or None
+        The inner batch function.
+    outer_batch_fn : function or None
+        The outer batch function.
+    inner_cost_fn : function
+        The inner cost function.
+    outer_cost_fn : function
+        The outer cost function.
+    variables : ivy.Container
+        The variables for optimization.
+    inner_grad_steps : int
+        Number of inner gradient steps.
+    inner_learning_rate : float
+        Inner learning rate.
+    inner_optimization_step : function
+        The inner optimization step function.
+    order : int
+        The order of computation.
+    average_across_steps : bool
+        Whether to average across steps.
+    inner_v : object
+        Inner variable.
+    keep_inner_v : bool
+        Whether to keep inner variable.
+    outer_v : object
+        Outer variable.
+    keep_outer_v : bool
+        Whether to keep outer variable.
+    return_inner_v : str or bool
+        Whether and which inner variables to return.
+    num_tasks : int
+        Number of tasks.
+    stop_gradients : bool
+        Whether to stop gradients during optimization.
+
+    Returns
+    -------
+    object or tuple
+        The computed cost and, optionally, gradients and updated inner variables.
+
+    Examples
+    --------
+    >>> # Example usage here
+    >>> pass
+    """
     inner_batch = batch
     outer_batch = batch
     if inner_batch_fn is not None:


Test patch:
Repository: ivy-llc/ivy

Issue:
amin



Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/paddle/tensor/math.py b/ivy/functional/frontends/paddle/tensor/math.py
--- a/ivy/functional/frontends/paddle/tensor/math.py
+++ b/ivy/functional/frontends/paddle/tensor/math.py
@@ -502,3 +502,10 @@ def tanh(x, name=None):
 @to_ivy_arrays_and_back
 def trunc(x, name=None):
     return ivy.trunc(x)
+
+@with_supported_dtypes(
+    {"2.5.1 and below": ("float32", "float64", "int32", "int64")}, "paddle"
+)
+@to_ivy_arrays_and_back
+def amin(x, axis=None, keepdim=False, name=None):    
+    return ivy.min(x, axis=axis, keepdims=keepdim)


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_math.py b/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_math.py
--- a/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_math.py
+++ b/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_math.py
@@ -2137,3 +2137,36 @@ def test_paddle_stanh(
         scale_a=scale_a,
         scale_b=scale_b,
     )
+
+
+# amin
+@handle_frontend_test(
+    fn_tree="paddle.tensor.math.amin",
+    dtype_and_x=helpers.dtype_values_axis(
+        available_dtypes=helpers.get_dtypes("valid"),
+        valid_axis=True,
+    ),
+    keepdim=st.booleans(),
+)
+def test_paddle_amin(
+    *,
+    dtype_and_x,
+    keepdim,
+    on_device,
+    fn_tree,
+    backend_fw,
+    frontend,
+    test_flags,
+):
+    input_dtype, x, axis = dtype_and_x
+    helpers.test_frontend_function(
+        input_dtypes=input_dtype,
+        frontend=frontend,
+        backend_to_test=backend_fw,
+        fn_tree=fn_tree,
+        test_flags=test_flags,
+        on_device=on_device,
+        x=x[0],
+        axis = axis,
+        keepdim = keepdim,
+    )
Repository: ivy-llc/ivy

Issue:
eigh
- [ ] #7479


Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/torch/linalg.py b/ivy/functional/frontends/torch/linalg.py
--- a/ivy/functional/frontends/torch/linalg.py
+++ b/ivy/functional/frontends/torch/linalg.py
@@ -370,3 +370,9 @@ def vector_norm(input, ord=2, dim=None, keepdim=False, *, dtype=None, out=None):
     return ivy.vector_norm(
         input, axis=dim, keepdims=keepdim, ord=ord, out=out, dtype=dtype
     )
+
+@with_supported_dtypes(
+    {"2.0.1 and below": ("float32", "float64", "complex32", "complex64", "complex128")}, "torch", 
+)
+def eigh(A, UPLO="L", *, out=None):
+    return ivy.eigh(A, UPLO=UPLO, out=out)
\ No newline at end of file


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_torch/test_linalg.py b/ivy_tests/test_ivy/test_frontends/test_torch/test_linalg.py
--- a/ivy_tests/test_ivy/test_frontends/test_torch/test_linalg.py
+++ b/ivy_tests/test_ivy/test_frontends/test_torch/test_linalg.py
@@ -1289,3 +1289,48 @@ def test_torch_cholesky_ex(
         input=x,
         upper=upper,
     )
+
+
+@handle_frontend_test(
+    fn_tree="torch.linalg.eigh",
+    dtype_and_x=_get_dtype_and_matrix(dtype="valid", square=True, invertible=True),
+    UPLO=st.sampled_from(("L", "U")),
+)
+def test_torch_eigh(
+    *,
+    dtype_and_x,
+    UPLO,
+    on_device,
+    fn_tree,
+    frontend,
+    test_flags,
+    backend_fw,
+):
+    dtype, x = dtype_and_x
+    x = np.array(x[0], dtype=dtype[0])
+    # make symmetric positive-definite beforehand
+    x = np.matmul(x.T, x) + np.identity(x.shape[0]) * 1e-3
+
+    ret, frontend_ret = helpers.test_frontend_function(
+        input_dtypes=dtype,
+        backend_to_test=backend_fw,
+        frontend=frontend,
+        test_flags=test_flags,
+        fn_tree=fn_tree,
+        on_device=on_device,
+        test_values=False,
+        a=x,
+        UPLO=UPLO,
+    )
+    ret = [ivy.to_numpy(x) for x in ret]
+    frontend_ret = [np.asarray(x) for x in frontend_ret]
+
+    L, Q = ret
+    frontend_L, frontend_Q = frontend_ret
+
+    assert_all_close(
+        ret_np=Q @ np.diag(L) @ Q.T,
+        ret_from_gt_np=frontend_Q @ np.diag(frontend_L) @ frontend_Q.T,
+        atol=1e-02,
+        
+    )
Repository: ivy-llc/ivy

Issue:
 rsqrt_



Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/paddle/tensor/math.py b/ivy/functional/frontends/paddle/tensor/math.py
--- a/ivy/functional/frontends/paddle/tensor/math.py
+++ b/ivy/functional/frontends/paddle/tensor/math.py
@@ -403,7 +403,7 @@ def rsqrt(x, name=None):
 
 @with_supported_dtypes({"2.5.1 and below": ("float32", "float64")}, "paddle")
 def rsqrt_(x, name=None):
-    return ivy.inplace_update(x, ivy.reciprocal(ivy.inplace_update(x, ivy.sqrt(x))))
+    return ivy.inplace_update(x, reciprocal(sqrt(x)))
 
 
 @with_unsupported_dtypes({"2.5.1 and below": ("float16", "bfloat16")}, "paddle")


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_math.py b/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_math.py
--- a/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_math.py
+++ b/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_math.py
@@ -1784,6 +1784,7 @@ def test_paddle_rsqrt(
         available_dtypes=helpers.get_dtypes("valid"),
     ),
 )
+
 def test_paddle_rsqrt_(
     *,
     dtype_and_x,
Repository: ivy-llc/ivy

Issue:
argmin



Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/jax/array.py b/ivy/functional/frontends/jax/array.py
--- a/ivy/functional/frontends/jax/array.py
+++ b/ivy/functional/frontends/jax/array.py
@@ -3,6 +3,7 @@
 # local
 import ivy
 import ivy.functional.frontends.jax as jax_frontend
+from ivy.func_wrapper import with_unsupported_dtypes
 
 
 class Array:
@@ -73,6 +74,7 @@ def astype(self, dtype):
                 f"Dtype {self.dtype} is not castable to {dtype}"
             )
 
+    @with_unsupported_dtypes({"2.5.1 and below": ("float16", "bfloat16")}, "paddle")
     def argmax(
         self,
         /,
@@ -88,6 +90,22 @@ def argmax(
             keepdims=keepdims,
         )
 
+    @with_unsupported_dtypes({"2.5.1 and below": ("float16", "bfloat16")}, "paddle")
+    def argmin(
+        self,
+        /,
+        *,
+        axis=None,
+        out=None,
+        keepdims=False,
+    ):
+        return jax_frontend.numpy.argmin(
+            self,
+            axis=axis,
+            out=out,
+            keepdims=keepdims,
+        )
+
     def conj(self, /):
         return jax_frontend.numpy.conj(self._ivy_array)
 


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_jax/test_array.py b/ivy_tests/test_ivy/test_frontends/test_jax/test_array.py
--- a/ivy_tests/test_ivy/test_frontends/test_jax/test_array.py
+++ b/ivy_tests/test_ivy/test_frontends/test_jax/test_array.py
@@ -347,6 +347,48 @@ def test_jax_array_argmax(
     )
 
 
+@handle_frontend_method(
+    class_tree=CLASS_TREE,
+    init_tree="jax.numpy.array",
+    method_name="argmin",
+    dtype_and_x=helpers.dtype_values_axis(
+        available_dtypes=helpers.get_dtypes("float"),
+        force_int_axis=True,
+        min_num_dims=1,
+        valid_axis=True,
+    ),
+    keepdims=st.booleans(),
+)
+def test_jax_array_argmin(
+    dtype_and_x,
+    keepdims,
+    on_device,
+    frontend,
+    frontend_method_data,
+    init_flags,
+    method_flags,
+    backend_fw,
+):
+    input_dtype, x, axis = dtype_and_x
+    helpers.test_frontend_method(
+        init_input_dtypes=input_dtype,
+        backend_to_test=backend_fw,
+        init_all_as_kwargs_np={
+            "object": x[0],
+        },
+        method_input_dtypes=input_dtype,
+        method_all_as_kwargs_np={
+            "axis": axis,
+            "keepdims": keepdims,
+        },
+        frontend=frontend,
+        frontend_method_data=frontend_method_data,
+        init_flags=init_flags,
+        method_flags=method_flags,
+        on_device=on_device,
+    )
+
+
 @handle_frontend_method(
     class_tree=CLASS_TREE,
     init_tree="jax.numpy.array",
Repository: ivy-llc/ivy

Issue:
conjugate



Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/numpy/ndarray/ndarray.py b/ivy/functional/frontends/numpy/ndarray/ndarray.py
--- a/ivy/functional/frontends/numpy/ndarray/ndarray.py
+++ b/ivy/functional/frontends/numpy/ndarray/ndarray.py
@@ -243,7 +243,7 @@ def compress(self, condition, axis=None, out=None):
             out=out,
         )
 
-    def conj(
+    def conjugate(
         self,
         /,
         out=None,
@@ -254,7 +254,7 @@ def conj(
         dtype=None,
         subok=True,
     ):
-        return np_frontend.conj(
+        return np_frontend.conjugate(
             self.ivy_array,
             out=out,
             where=where,
@@ -603,7 +603,7 @@ def ptp(self, *, axis=None, out=None, keepdims=False):
     def item(self, *args):
         if len(args) == 0:
             return self[0].ivy_array.to_scalar()
-        elif len(args) == 1 and type(args[0]) == int:
+        elif len(args) == 1 and isinstance(args[0], int):
             index = args[0]
             return self.ivy_array.flatten()[index].to_scalar()
         else:


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_numpy/test_ndarray/test_ndarray.py b/ivy_tests/test_ivy/test_frontends/test_numpy/test_ndarray/test_ndarray.py
--- a/ivy_tests/test_ivy/test_frontends/test_numpy/test_ndarray/test_ndarray.py
+++ b/ivy_tests/test_ivy/test_frontends/test_numpy/test_ndarray/test_ndarray.py
@@ -910,12 +910,12 @@ def test_numpy_ndarray_compress(
 @handle_frontend_method(
     class_tree=CLASS_TREE,
     init_tree="numpy.array",
-    method_name="conj",
+    method_name="conjugate",
     dtype_and_x=helpers.dtype_values_axis(
         available_dtypes=helpers.get_dtypes("real_and_complex"),
     ),
 )
-def test_numpy_ndarray_conj(
+def test_numpy_ndarray_conjugate(
     dtype_and_x,
     on_device,
     frontend,
Repository: ivy-llc/ivy

Issue:
clone



Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/paddle/tensor/creation.py b/ivy/functional/frontends/paddle/tensor/creation.py
--- a/ivy/functional/frontends/paddle/tensor/creation.py
+++ b/ivy/functional/frontends/paddle/tensor/creation.py
@@ -29,6 +29,14 @@ def assign(x, output=None):
     return ret
 
 
+@with_unsupported_dtypes(
+    {"2.5.1 and below": ("bfloat16", "uint16", "uint32", "uint64")}, "paddle"
+)
+@to_ivy_arrays_and_back
+def clone(x):
+    return ivy.copy_array(x)
+
+
 @with_supported_dtypes(
     {"2.5.1 and below": ("float32", "float64")},
     "paddle",


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_creation.py b/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_creation.py
--- a/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_creation.py
+++ b/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_creation.py
@@ -760,3 +760,29 @@ def test_paddle_linspace(
         num=num,
         dtype=dtype[0],
     )
+
+
+# clone
+@handle_frontend_test(
+    fn_tree="paddle.clone",
+    dtype_and_x=helpers.dtype_and_values(available_dtypes=helpers.get_dtypes("valid")),
+)
+def test_paddle_clone(
+    *,
+    dtype_and_x,
+    test_flags,
+    frontend,
+    backend_fw,
+    fn_tree,
+    on_device,
+):
+    input_dtype, x = dtype_and_x
+    helpers.test_frontend_function(
+        input_dtypes=input_dtype,
+        backend_to_test=backend_fw,
+        frontend=frontend,
+        test_flags=test_flags,
+        fn_tree=fn_tree,
+        on_device=on_device,
+        x=x[0],
+    )