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

Issue:
unstack



Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/paddle/tensor/manipulation.py b/ivy/functional/frontends/paddle/tensor/manipulation.py
--- a/ivy/functional/frontends/paddle/tensor/manipulation.py
+++ b/ivy/functional/frontends/paddle/tensor/manipulation.py
@@ -147,6 +147,10 @@ def roll(x, shifts, axis=None, name=None):
     "paddle",
 )
 @to_ivy_arrays_and_back
+def unstack(x, axis=0, name=None):
+    return ivy.unstack(x, axis=axis)
+
+
 def take_along_axis(arr, indices, axis):
     return ivy.take_along_axis(arr, indices, axis)
 


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_manipulation.py b/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_manipulation.py
--- a/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_manipulation.py
+++ b/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_manipulation.py
@@ -523,6 +523,43 @@ def test_paddle_gather(
     )
 
 
+# unstack
+@handle_frontend_test(
+    fn_tree="paddle.unstack",
+    dtypes_values=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("numeric"),
+        min_num_dims=2,
+        max_num_dims=2,
+        max_dim_size=1,
+    ),
+    number_positional_args=st.just(1),
+    axis=st.integers(-1, 0),
+    test_with_out=st.just(False),
+)
+def test_paddle_unstack(
+    *,
+    dtypes_values,
+    axis,
+    on_device,
+    fn_tree,
+    backend_fw,
+    frontend,
+    test_flags,
+):
+    x_dtype, x = dtypes_values
+    axis = axis
+    helpers.test_frontend_function(
+        input_dtypes=x_dtype,
+        backend_to_test=backend_fw,
+        frontend=frontend,
+        test_flags=test_flags,
+        fn_tree=fn_tree,
+        on_device=on_device,
+        x=x[0],
+        axis=axis,
+    )
+
+
 # flip
 @st.composite
 def _dtype_x_axis(draw, **kwargs):
Repository: ivy-llc/ivy

Issue:
roll



Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/paddle/tensor/manipulation.py b/ivy/functional/frontends/paddle/tensor/manipulation.py
--- a/ivy/functional/frontends/paddle/tensor/manipulation.py
+++ b/ivy/functional/frontends/paddle/tensor/manipulation.py
@@ -114,3 +114,21 @@ def broadcast_to(x, shape, name=None):
 @to_ivy_arrays_and_back
 def gather(params, indices, axis=-1, batch_dims=0, name=None):
     return ivy.gather(params, indices, axis=axis, batch_dims=batch_dims)
+
+
+@with_supported_dtypes(
+    {
+        "2.5.0 and below": (
+            "float32",
+            "float64",
+            "int32",
+            "int64",
+            "complex64",
+            "complex128",
+        )
+    },
+    "paddle",
+)
+@to_ivy_arrays_and_back
+def roll(x, shifts, axis=None, name=None):
+    return ivy.roll(x, shifts, axis=axis)


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_manipulation.py b/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_manipulation.py
--- a/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_manipulation.py
+++ b/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_manipulation.py
@@ -562,3 +562,40 @@ def test_paddle_flip(
         x=x[0],
         axis=axis,
     )
+
+
+# roll
+@handle_frontend_test(
+    fn_tree="paddle.roll",
+    dtype_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("numeric"),
+        min_num_dims=2,
+        min_dim_size=2,
+    ),
+    shift=helpers.ints(min_value=1, max_value=10),
+    axis=helpers.ints(min_value=-1, max_value=1),
+    test_with_out=st.just(False),
+)
+def test_paddle_roll(
+    *,
+    dtype_and_x,
+    shift,
+    axis,
+    on_device,
+    fn_tree,
+    frontend,
+    test_flags,
+    backend_fw,
+):
+    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],
+        shifts=shift,
+        axis=axis,
+    )
Repository: ivy-llc/ivy

Issue:
erfc



Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/torch/pointwise_ops.py b/ivy/functional/frontends/torch/pointwise_ops.py
--- a/ivy/functional/frontends/torch/pointwise_ops.py
+++ b/ivy/functional/frontends/torch/pointwise_ops.py
@@ -528,6 +528,12 @@ def erf(input, *, out=None):
     return ivy.erf(input, out=out)
 
 
+@with_unsupported_dtypes({"2.0.1 and below": ("float16", "complex")}, "torch")
+@to_ivy_arrays_and_back
+def erfc(input, *, out=None):
+    return 1.0 - ivy.erf(input, out=out)
+
+
 @to_ivy_arrays_and_back
 def sgn(input, *, out=None):
     if ivy.is_complex_dtype(input.dtype):


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_torch/test_pointwise_ops.py b/ivy_tests/test_ivy/test_frontends/test_torch/test_pointwise_ops.py
--- a/ivy_tests/test_ivy/test_frontends/test_torch/test_pointwise_ops.py
+++ b/ivy_tests/test_ivy/test_frontends/test_torch/test_pointwise_ops.py
@@ -2459,6 +2459,32 @@ def test_torch_erf(
     )
 
 
+# erfc
+@handle_frontend_test(
+    fn_tree="torch.erfc",
+    dtype_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("float"),
+    ),
+)
+def test_torch_erfc(
+    *,
+    dtype_and_x,
+    on_device,
+    fn_tree,
+    frontend,
+    test_flags,
+):
+    input_dtype, x = dtype_and_x
+    helpers.test_frontend_function(
+        input_dtypes=input_dtype,
+        frontend=frontend,
+        test_flags=test_flags,
+        fn_tree=fn_tree,
+        on_device=on_device,
+        input=x[0],
+    )
+
+
 @handle_frontend_test(
     fn_tree="torch.frexp",
     dtype_and_x=helpers.dtype_and_values(
Repository: ivy-llc/ivy

Issue:
compress



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
@@ -215,6 +215,14 @@ def clip(
             subok=subok,
         )
 
+    def compress(self, condition, axis=None, out=None):
+        return np_frontend.compress(
+            condition=condition,
+            a=self,
+            axis=axis,
+            out=out,
+        )
+
     def conj(
         self,
         /,


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
@@ -732,6 +732,55 @@ def test_numpy_instance_clip(
     )
 
 
+@handle_frontend_method(
+    class_tree=CLASS_TREE,
+    init_tree="numpy.array",
+    method_name="compress",
+    dtype_arr_ax=helpers.dtype_values_axis(
+        available_dtypes=helpers.get_dtypes("valid"),
+        min_num_dims=1,
+        max_num_dims=5,
+        min_dim_size=10,
+        max_dim_size=100,
+        valid_axis=True,
+        force_int_axis=True,
+    ),
+    condition=helpers.array_values(
+        dtype=helpers.get_dtypes("bool"),
+        shape=helpers.get_shape(
+            min_num_dims=1, max_num_dims=1, min_dim_size=1, max_dim_size=5
+        ),
+    ),
+)
+def test_numpy_ndarray_compress(
+    dtype_arr_ax,
+    condition,
+    frontend_method_data,
+    init_flags,
+    method_flags,
+    frontend,
+    on_device,
+):
+    input_dtypes, arr, ax = dtype_arr_ax
+    helpers.test_frontend_method(
+        init_input_dtypes=input_dtypes,
+        init_all_as_kwargs_np={
+            "object": arr[0],
+        },
+        method_input_dtypes=input_dtypes,
+        method_all_as_kwargs_np={
+            "condition": condition,
+            "axis": ax,
+            "out": None,
+        },
+        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="numpy.array",
Repository: ivy-llc/ivy

Issue:
log_sigmoid



Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/paddle/nn/functional/activation.py b/ivy/functional/frontends/paddle/nn/functional/activation.py
--- a/ivy/functional/frontends/paddle/nn/functional/activation.py
+++ b/ivy/functional/frontends/paddle/nn/functional/activation.py
@@ -225,5 +225,9 @@ def leaky_relu(x, negative_slope=0.01, name=None):
 
 @with_supported_dtypes({"2.5.0 and below": ("float32", "float64")}, "paddle")
 @to_ivy_arrays_and_back
+def log_sigmoid(x, name=None):
+    return -ivy.softplus(-x)
+
+
 def silu(x, name=None):
     return ivy.silu(x)


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_paddle/test_nn/test_functional/test_activation.py b/ivy_tests/test_ivy/test_frontends/test_paddle/test_nn/test_functional/test_activation.py
--- a/ivy_tests/test_ivy/test_frontends/test_paddle/test_nn/test_functional/test_activation.py
+++ b/ivy_tests/test_ivy/test_frontends/test_paddle/test_nn/test_functional/test_activation.py
@@ -568,6 +568,36 @@ def test_paddle_leaky_relu(
     )
 
 
+# log_sigmoid
+@handle_frontend_test(
+    fn_tree="paddle.nn.functional.log_sigmoid",
+    dtype_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("float"),
+        large_abs_safety_factor=3,
+        small_abs_safety_factor=3,
+        safety_factor_scale="linear",
+    ),
+    test_with_out=st.just(False),
+)
+def test_paddle_log_sigmoid(
+    *,
+    dtype_and_x,
+    frontend,
+    test_flags,
+    fn_tree,
+    on_device,
+):
+    input_dtype, x = dtype_and_x
+    helpers.test_frontend_function(
+        input_dtypes=input_dtype,
+        frontend=frontend,
+        test_flags=test_flags,
+        fn_tree=fn_tree,
+        on_device=on_device,
+        x=x[0],
+    )
+
+
 # silu
 @handle_frontend_test(
     fn_tree="paddle.nn.functional.silu",
Repository: ivy-llc/ivy

Issue:
eigvals



Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/tensorflow/linalg.py b/ivy/functional/frontends/tensorflow/linalg.py
--- a/ivy/functional/frontends/tensorflow/linalg.py
+++ b/ivy/functional/frontends/tensorflow/linalg.py
@@ -34,6 +34,15 @@ def eigh(tensor, name=None):
     return ivy.eigh(tensor)
 
 
+@to_ivy_arrays_and_back
+@with_supported_dtypes(
+    {"2.13.0 and below": ("float32", "float64", "complex64", "complex128")},
+    "tensorflow",
+)
+def eigvals(tensor, name=None):
+    return ivy.eigvals(tensor)
+
+
 @to_ivy_arrays_and_back
 def eigvalsh(tensor, name=None):
     return ivy.eigvalsh(tensor)


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_tensorflow/test_linalg.py b/ivy_tests/test_ivy/test_frontends/test_tensorflow/test_linalg.py
--- a/ivy_tests/test_ivy/test_frontends/test_tensorflow/test_linalg.py
+++ b/ivy_tests/test_ivy/test_frontends/test_tensorflow/test_linalg.py
@@ -80,6 +80,50 @@ def test_tensorflow_eigh(
     )
 
 
+@handle_frontend_test(
+    fn_tree="tensorflow.linalg.eigvals",
+    dtype_and_input=_get_dtype_and_matrix(),
+    test_with_out=st.just(False),
+)
+def test_tensorflow_eigvals(
+    *,
+    dtype_and_input,
+    frontend,
+    test_flags,
+    fn_tree,
+    on_device,
+):
+    input_dtype, x = dtype_and_input
+    assume(matrix_is_stable(x[0]))
+    if x[0].dtype == ivy.float32:
+        x[0] = x[0].astype("float64")
+        input_dtype = [ivy.float64]
+    ret, frontend_ret = helpers.test_frontend_function(
+        input_dtypes=input_dtype,
+        frontend=frontend,
+        test_flags=test_flags,
+        fn_tree=fn_tree,
+        on_device=on_device,
+        tensor=x[0],
+        test_values=False,
+    )
+
+    ret = ivy.to_numpy(ret)
+    ret = ret.round(6)
+    ret = np.sort(ret)
+    frontend_ret = frontend_ret[0].numpy()
+    frontend_ret = frontend_ret.round(6)
+    frontend_ret = np.sort(frontend_ret)
+
+    assert_all_close(
+        ret_np=ret,
+        ret_from_gt_np=frontend_ret,
+        rtol=1e-06,
+        atol=1e-06,
+        ground_truth_backend=frontend,
+    )
+
+
 @handle_frontend_test(
     fn_tree="tensorflow.linalg.eigvalsh",
     dtype_and_input=_get_dtype_and_matrix(),
Repository: ivy-llc/ivy

Issue:
dist



Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/paddle/tensor/linalg.py b/ivy/functional/frontends/paddle/tensor/linalg.py
--- a/ivy/functional/frontends/paddle/tensor/linalg.py
+++ b/ivy/functional/frontends/paddle/tensor/linalg.py
@@ -176,3 +176,9 @@ def transpose(x, perm, name=None):
 @to_ivy_arrays_and_back
 def bincount(x, weights=None, minlength=0, name=None):
     return ivy.bincount(x, weights=weights, minlength=minlength)
+
+@with_supported_dtypes({"2.4.1 and above": ("float64","float32")}, "paddle")
+@to_ivy_arrays_and_back
+def dist(x, y, p=2):
+    ret = ivy.vector_norm(ivy.subtract(x, y), ord=p)
+    return ivy.reshape(ret, (1,))


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_linalg.py b/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_linalg.py
--- a/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_linalg.py
+++ b/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_linalg.py
@@ -768,3 +768,40 @@ def test_paddle_bincount(
         weights=None,
         minlength=0,
     )
+
+
+@handle_frontend_test(
+    fn_tree="paddle.dist",
+    dtype_and_input=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("float"),
+        num_arrays=2,
+        shared_dtype=True,
+        min_value=-1e04,
+        max_value=1e04,
+        allow_inf=False,
+    ),
+    p=helpers.floats(min_value=1.0, max_value=10.0),
+)
+def test_paddle_dist(
+    *,
+    dtype_and_input,
+    p,
+    on_device,
+    fn_tree,
+    backend_fw,
+    frontend,
+    test_flags,
+):
+    input_dtype, x = dtype_and_input
+    helpers.test_frontend_function(
+        input_dtypes=input_dtype,
+        frontend=frontend,
+        test_flags=test_flags,
+        backend_to_test=backend_fw,
+        fn_tree=fn_tree,
+        on_device=on_device,
+        x=x[0],
+        y=x[1],
+        p=p,
+    )
+
Repository: ivy-llc/ivy

Issue:
T



Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/jax/devicearray.py b/ivy/functional/frontends/jax/devicearray.py
--- a/ivy/functional/frontends/jax/devicearray.py
+++ b/ivy/functional/frontends/jax/devicearray.py
@@ -41,6 +41,10 @@ def shape(self):
     def at(self):
         return jax_frontend._src.numpy.lax_numpy._IndexUpdateHelper(self.ivy_array)
 
+    @property
+    def T(self):
+        return self.ivy_array.T
+
     # Instance Methods #
     # ---------------- #
 


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_jax/test_devicearray.py b/ivy_tests/test_ivy/test_frontends/test_jax/test_devicearray.py
--- a/ivy_tests/test_ivy/test_frontends/test_jax/test_devicearray.py
+++ b/ivy_tests/test_ivy/test_frontends/test_jax/test_devicearray.py
@@ -59,6 +59,30 @@ def test_jax_devicearray_property_shape(
     assert x.shape == shape
 
 
+@st.composite
+def _transpose_helper(draw):
+    dtype_x = draw(
+        helpers.dtype_and_values(
+            available_dtypes=helpers.get_dtypes("valid", prune_function=False),
+            min_num_dims=2,
+            max_num_dims=2,
+            min_dim_size=2,
+        )
+    )
+
+    _, data = dtype_x
+    x = data[0]
+    xT = np.transpose(x)
+    return x, xT
+
+
+@given(x_transpose=_transpose_helper())
+def test_jax_devicearray_property_T(x_transpose):
+    x, xT = x_transpose
+    x = DeviceArray(x)
+    assert np.array_equal(x.T, xT)
+
+
 @st.composite
 def _at_helper(draw):
     _, data, shape = draw(
Repository: ivy-llc/ivy

Issue:
any



Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/jax/devicearray.py b/ivy/functional/frontends/jax/devicearray.py
--- a/ivy/functional/frontends/jax/devicearray.py
+++ b/ivy/functional/frontends/jax/devicearray.py
@@ -128,6 +128,11 @@ def sort(self, axis=-1, order=None):
     def argsort(self, axis=-1, kind="stable", order=None):
         return jax_frontend.numpy.argsort(self, axis=axis, kind=kind, order=order)
 
+    def any(self, *, axis=None, out=None, keepdims=False, where=None):
+        return jax_frontend.numpy.any(
+            self._ivy_array, axis=axis, keepdims=keepdims, out=out, where=where
+        )
+
     def __add__(self, other):
         return jax_frontend.numpy.add(self, other)
 


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_jax/test_devicearray.py b/ivy_tests/test_ivy/test_frontends/test_jax/test_devicearray.py
--- a/ivy_tests/test_ivy/test_frontends/test_jax/test_devicearray.py
+++ b/ivy_tests/test_ivy/test_frontends/test_jax/test_devicearray.py
@@ -565,6 +565,46 @@ def test_jax_devicearray_argsort(
     )
 
 
+@handle_frontend_method(
+    class_tree=CLASS_TREE,
+    init_tree="jax.numpy.array",
+    method_name="any",
+    dtype_x_axis=helpers.dtype_values_axis(
+        available_dtypes=helpers.get_dtypes("valid"),
+        force_int_axis=True,
+        valid_axis=True,
+        min_num_dims=1,
+    ),
+    keepdims=st.booleans(),
+)
+def test_jax_devicearray_any(
+    dtype_x_axis,
+    keepdims,
+    on_device,
+    frontend,
+    frontend_method_data,
+    init_flags,
+    method_flags,
+):
+    input_dtype, x, axis = dtype_x_axis
+    helpers.test_frontend_method(
+        init_input_dtypes=input_dtype,
+        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:
cholesky_ex



Discussion:
This issue has been labelled as stale. If you would like to continue working on this issue, then please add another comment or create a PR . If a PR has already been created which refers to this issue, then you should explicitly mention this issue in the relevant PR. For more information please refer to this page in our documentation: https://lets-unify.ai/ivy/contributing/1_the_basics.html#todo-list-issues
I'm working 

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
@@ -355,3 +355,16 @@ def solve_ex(A, B, *, left=True, check_errors=False, out=None):
             info = ivy.ones(A.shape[:-2], dtype=ivy.int32)
 
             return result, info
+@to_ivy_arrays_and_back
+def cholesky_ex(input, *, upper=False, check_errors=False, out=None):
+    try:
+        matrix = ivy.cholesky(input, upper=upper, out=out)
+        info = ivy.zeros(input.shape[:-2], dtype=ivy.int32)
+        return matrix, info
+    except RuntimeError as e:
+        if check_errors:
+            raise RuntimeError(e)
+        else:
+            matrix = input * math.nan
+            info = ivy.ones(input.shape[:-2], dtype=ivy.int32)
+            return matrix, info
\ 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
@@ -1223,3 +1223,30 @@ def test_torch_solve_ex(
         left=left,
         check_errors=check_errors,
     )
+@handle_frontend_test(
+    fn_tree="torch.linalg.cholesky_ex",
+    dtype_and_x=_get_dtype_and_square_matrix(),
+    upper=st.booleans(),
+)
+def test_torch_cholesky_ex(
+    *,
+    dtype_and_x,
+    upper,
+    on_device,
+    fn_tree,
+    frontend,
+    test_flags,
+):
+    dtype, x = dtype_and_x
+    x = np.matmul(x.T, x) + np.identity(x.shape[0])  # make symmetric positive-definite
+
+    helpers.test_frontend_function(
+        input_dtypes=dtype,
+        frontend=frontend,
+        test_flags=test_flags,
+        fn_tree=fn_tree,
+        on_device=on_device,
+        rtol=1e-01,
+        input=x,
+        upper=upper,
+    )
\ No newline at end of file
Repository: ivy-llc/ivy

Issue:
argsort



Discussion:
This issue has been labelled as stale. If you would like to continue working on this issue, then please add another comment or create a PR . If a PR has already been created which refers to this issue, then you should explicitly mention this issue in the relevant PR. For more information please refer to this page in our documentation: https://lets-unify.ai/ivy/contributing/1_the_basics.html#todo-list-issues

Resolving patch:
diff --git a/ivy/functional/frontends/jax/devicearray.py b/ivy/functional/frontends/jax/devicearray.py
--- a/ivy/functional/frontends/jax/devicearray.py
+++ b/ivy/functional/frontends/jax/devicearray.py
@@ -116,6 +116,9 @@ def sort(self, axis=-1, order=None):
             order=order,
         )
 
+    def argsort(self, axis=-1, kind="stable", order=None):
+        return jax_frontend.numpy.argsort(self, axis=axis, kind=kind, order=order)
+
     def __add__(self, other):
         return jax_frontend.numpy.add(self, other)
 


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_jax/test_devicearray.py b/ivy_tests/test_ivy/test_frontends/test_jax/test_devicearray.py
--- a/ivy_tests/test_ivy/test_frontends/test_jax/test_devicearray.py
+++ b/ivy_tests/test_ivy/test_frontends/test_jax/test_devicearray.py
@@ -470,6 +470,44 @@ def test_jax_devicearray_sort(
     )
 
 
+@handle_frontend_method(
+    class_tree=CLASS_TREE,
+    init_tree="jax.numpy.array",
+    method_name="argsort",
+    dtype_x_axis=helpers.dtype_values_axis(
+        available_dtypes=helpers.get_dtypes("numeric"),
+        min_axis=-1,
+        max_axis=0,
+        min_num_dims=1,
+        force_int_axis=True,
+    ),
+)
+def test_jax_devicearray_argsort(
+    dtype_x_axis,
+    on_device,
+    frontend,
+    frontend_method_data,
+    init_flags,
+    method_flags,
+):
+    input_dtype, x, axis = dtype_x_axis
+    helpers.test_frontend_method(
+        init_input_dtypes=input_dtype,
+        init_all_as_kwargs_np={
+            "object": x[0],
+        },
+        method_input_dtypes=input_dtype,
+        method_all_as_kwargs_np={
+            "axis": axis,
+        },
+        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",