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

Issue:
set_min_base



Discussion:

Resolving patch:
diff --git a/ivy/functional/ivy/general.py b/ivy/functional/ivy/general.py
--- a/ivy/functional/ivy/general.py
+++ b/ivy/functional/ivy/general.py
@@ -2171,18 +2171,29 @@ def set_min_base(val: float) -> None:
 
     Examples
     --------
+
+    Retrieve the minimum base
     >>> x = ivy.min_base
     >>> print(x)
     1e-05
 
+    Set the minimum base to 1e-04:
     >>> ivy.set_min_base(1e-04)
+
+    Retrieve the minimum base:
     >>> y = ivy.min_base
     >>> print(y)
     1e-04
     """
     global min_base_stack
+
+    # Ensure val is an instance of 'float' or 'int'
     ivy.utils.assertions.check_isinstance(val, (int, float))
+
+    # Access and modify min_base_stack
     min_base_stack.append(val)
+
+    # Set the min_base attribute
     ivy.__setattr__("min_base", val, True)
 
 


Test patch:
Repository: ivy-llc/ivy

Issue:
kaiser_window



Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/torch/spectral_ops.py b/ivy/functional/frontends/torch/spectral_ops.py
--- a/ivy/functional/frontends/torch/spectral_ops.py
+++ b/ivy/functional/frontends/torch/spectral_ops.py
@@ -44,3 +44,18 @@ def blackman_window(
     requires_grad=False
 ):
     return ivy.blackman_window(window_length, periodic=periodic, dtype=dtype)
+
+
+@to_ivy_arrays_and_back
+@with_supported_dtypes({"2.51.0 and below": ("float32", "float64")}, "torch")
+def kaiser_window(
+    window_length,
+    periodic=True,
+    beta=12.0,
+    *,
+    dtype=None,
+    layout=None,
+    device=None,
+    requires_grad=False
+):
+    return ivy.kaiser_window(window_length, periodic=periodic, beta=beta, dtype=dtype)


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_torch/test_spectral_ops.py b/ivy_tests/test_ivy/test_frontends/test_torch/test_spectral_ops.py
--- a/ivy_tests/test_ivy/test_frontends/test_torch/test_spectral_ops.py
+++ b/ivy_tests/test_ivy/test_frontends/test_torch/test_spectral_ops.py
@@ -64,3 +64,38 @@ def test_torch_blackman_window(
         rtol=1e-02,
         atol=1e-02,
     )
+
+
+@handle_frontend_test(
+    window_length=helpers.ints(min_value=1, max_value=100),
+    dtype=helpers.get_dtypes("float", full=False),
+    fn_tree="torch.kaiser_window",
+    periodic=st.booleans(),
+    beta=helpers.floats(min_value=1, max_value=20),
+)
+def test_torch_kaiser_window(
+    *,
+    window_length,
+    dtype,
+    periodic,
+    beta,
+    on_device,
+    fn_tree,
+    frontend,
+    backend_fw,
+    test_flags,
+):
+    helpers.test_frontend_function(
+        input_dtypes=[],
+        on_device=on_device,
+        frontend=frontend,
+        backend_to_test=backend_fw,
+        test_flags=test_flags,
+        fn_tree=fn_tree,
+        window_length=window_length,
+        periodic=periodic,
+        beta=beta,
+        dtype=dtype[0],
+        rtol=1e-02,
+        atol=1e-02,
+    )
Repository: ivy-llc/ivy

Issue:
positive



Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/torch/tensor.py b/ivy/functional/frontends/torch/tensor.py
--- a/ivy/functional/frontends/torch/tensor.py
+++ b/ivy/functional/frontends/torch/tensor.py
@@ -774,6 +774,10 @@ def is_cuda(self):
     def is_meta(self):
         return "meta" in ivy.dev(self.ivy_array)
 
+    @with_unsupported_dtypes({"2.1.0 and below": ("uint16", "bool")}, "torch")
+    def positive(self):
+        return torch_frontend.positive(self)
+
     @with_unsupported_dtypes({"2.1.0 and below": ("bfloat16",)}, "torch")
     def pow(self, exponent):
         return torch_frontend.pow(self, exponent)


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_torch/test_tensor.py b/ivy_tests/test_ivy/test_frontends/test_torch/test_tensor.py
--- a/ivy_tests/test_ivy/test_frontends/test_torch/test_tensor.py
+++ b/ivy_tests/test_ivy/test_frontends/test_torch/test_tensor.py
@@ -10658,6 +10658,44 @@ def test_torch_tensor_permute(
     )
 
 
+# positive
+@handle_frontend_method(
+    class_tree=CLASS_TREE,
+    init_tree="torch.tensor",
+    method_name="positive",
+    dtype_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("float"),
+        min_value=-1e04,
+        max_value=1e04,
+        allow_inf=False,
+    ),
+)
+def test_torch_tensor_positive(
+    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={},
+        frontend_method_data=frontend_method_data,
+        init_flags=init_flags,
+        method_flags=method_flags,
+        frontend=frontend,
+        on_device=on_device,
+    )
+
+
 # pow
 @handle_frontend_method(
     class_tree=CLASS_TREE,
Repository: ivy-llc/ivy

Issue:
bartlett



Discussion:
Hello there can i work on this during HACKTOBERFEST?

Resolving patch:
diff --git a/ivy/functional/frontends/jax/numpy/manipulations.py b/ivy/functional/frontends/jax/numpy/manipulations.py
--- a/ivy/functional/frontends/jax/numpy/manipulations.py
+++ b/ivy/functional/frontends/jax/numpy/manipulations.py
@@ -37,6 +37,21 @@ def atleast_3d(*arys):
     return ivy.atleast_3d(*arys)
 
 
+@to_ivy_arrays_and_back
+def bartlett(M):
+    if M < 1:
+        return ivy.array([])
+    if M == 1:
+        return ivy.ones(M, dtype=ivy.float64)
+    res = ivy.arange(0, M)
+    res = ivy.where(
+        ivy.less_equal(res, (M - 1) / 2.0),
+        2.0 * res / (M - 1),
+        2.0 - 2.0 * res / (M - 1),
+    )
+    return res
+
+
 @to_ivy_arrays_and_back
 def blackman(M):
     if M < 1:


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_jax/test_numpy/test_manipulations.py b/ivy_tests/test_ivy/test_frontends/test_jax/test_numpy/test_manipulations.py
--- a/ivy_tests/test_ivy/test_frontends/test_jax/test_numpy/test_manipulations.py
+++ b/ivy_tests/test_ivy/test_frontends/test_jax/test_numpy/test_manipulations.py
@@ -498,6 +498,30 @@ def test_jax_atleast_3d(
     )
 
 
+# bartlett
+@handle_frontend_test(
+    fn_tree="jax.numpy.bartlett",
+    m=helpers.ints(min_value=0, max_value=20),
+)
+def test_jax_bartlett(
+    m,
+    frontend,
+    backend_fw,
+    test_flags,
+    fn_tree,
+    on_device,
+):
+    helpers.test_frontend_function(
+        input_dtypes=["int64"],
+        backend_to_test=backend_fw,
+        frontend=frontend,
+        test_flags=test_flags,
+        fn_tree=fn_tree,
+        on_device=on_device,
+        M=m,
+    )
+
+
 # blackman
 @handle_frontend_test(
     fn_tree="jax.numpy.blackman",
Repository: ivy-llc/ivy

Issue:
Remove redefined `method` in `ivy\__init__.py`
In the file [`ivy\__init__.py`](https://github.com/unifyai/ivy/blob/main/ivy/__init__.py), the following method is repeated at 2 places.
https://github.com/unifyai/ivy/blob/4e8178868cd69ff9ae1ee7f371fa984c8be1bae0/ivy/__init__.py#L382
https://github.com/unifyai/ivy/blob/4e8178868cd69ff9ae1ee7f371fa984c8be1bae0/ivy/__init__.py#L403
It can be removed at one of the places.


Discussion:

Resolving patch:
diff --git a/ivy/__init__.py b/ivy/__init__.py
--- a/ivy/__init__.py
+++ b/ivy/__init__.py
@@ -399,10 +399,6 @@ def index(self, index):
         else:
             return self._shape[index]
 
-    @property
-    def shape(self):
-        return self._shape
-
     def as_dimension(self):
         if isinstance(self._shape, Shape):
             return self._shape


Test patch:
Repository: ivy-llc/ivy

Issue:
all



Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/paddle/math.py b/ivy/functional/frontends/paddle/math.py
--- a/ivy/functional/frontends/paddle/math.py
+++ b/ivy/functional/frontends/paddle/math.py
@@ -51,6 +51,12 @@ def addmm(input, x, y, beta=1.0, alpha=1.0, name=None):
     return value
 
 
+@with_supported_dtypes({"2.5.0 and below": "bool"}, "paddle")
+@to_ivy_arrays_and_back
+def all(x, axis, keepdim=False, name=None):
+    return ivy.all(x, axis=axis, keepdims=keepdim)
+
+
 @with_supported_dtypes(
     {"2.5.1 and below": ("float32", "float64", "int32", "int64")}, "paddle"
 )


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_paddle/test_math.py b/ivy_tests/test_ivy/test_frontends/test_paddle/test_math.py
--- a/ivy_tests/test_ivy/test_frontends/test_paddle/test_math.py
+++ b/ivy_tests/test_ivy/test_frontends/test_paddle/test_math.py
@@ -227,6 +227,42 @@ def test_paddle_addmm(
     )
 
 
+# all
+@handle_frontend_test(
+    fn_tree="paddle.all",
+    dtype_and_x=helpers.dtype_values_axis(
+        available_dtypes=["bool"],
+        valid_axis=True,
+        allow_neg_axes=True,
+        force_int_axis=True,
+        min_num_dims=1,
+    ),
+    keepdim=st.booleans(),
+)
+def test_paddle_all(
+    *,
+    dtype_and_x,
+    keepdim,
+    on_device,
+    fn_tree,
+    frontend,
+    test_flags,
+    backend_fw,
+):
+    input_dtype, x, axis = 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,
+        backend_to_test=backend_fw,
+        x=x[0],
+        axis=axis,
+        keepdim=keepdim,
+    )
+
+
 # amax
 @handle_frontend_test(
     fn_tree="paddle.amax",
Repository: ivy-llc/ivy

Issue:
inverse



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
Looking at getting the PR raised for this now.

Resolving patch:
diff --git a/ivy/functional/frontends/paddle/math.py b/ivy/functional/frontends/paddle/math.py
--- a/ivy/functional/frontends/paddle/math.py
+++ b/ivy/functional/frontends/paddle/math.py
@@ -325,6 +325,12 @@ def inner(x, y, name=None):
     return result
 
 
+@with_supported_dtypes({"2.5.1 and below": ("float32", "float64")}, "paddle")
+@to_ivy_arrays_and_back
+def inverse(x, name=None):
+    return ivy.inv(x)
+
+
 @with_supported_dtypes(
     {"2.5.1 and below": ("float16", "float32", "float64", "int32", "int64")}, "paddle"
 )


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_paddle/test_math.py b/ivy_tests/test_ivy/test_frontends/test_paddle/test_math.py
--- a/ivy_tests/test_ivy/test_frontends/test_paddle/test_math.py
+++ b/ivy_tests/test_ivy/test_frontends/test_paddle/test_math.py
@@ -1,6 +1,8 @@
 # global
 from hypothesis import strategies as st
 import hypothesis.extra.numpy as nph
+import numpy as np
+import sys
 
 # local
 import ivy_tests.test_ivy.helpers as helpers
@@ -1322,6 +1324,45 @@ def test_paddle_inner(
     )
 
 
+# inverse
+@handle_frontend_test(
+    fn_tree="paddle.inverse",
+    dtype_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("float"),
+        min_value=-100.0,
+        max_value=100.0,
+        shape=helpers.ints(min_value=2, max_value=10).map(lambda x: tuple([x, x])),
+    ).filter(
+        lambda x: "float16" not in x[0]
+        and "bfloat16" not in x[0]
+        and np.linalg.det(np.asarray(x[1][0])) != 0
+        and np.linalg.cond(x[1][0]) < 1 / sys.float_info.epsilon
+    ),
+    test_with_out=st.just(False),
+)
+def test_paddle_inverse(
+    *,
+    dtype_and_x,
+    on_device,
+    fn_tree,
+    frontend,
+    test_flags,
+    backend_fw,
+):
+    dtype, x = dtype_and_x
+    helpers.test_frontend_function(
+        input_dtypes=dtype,
+        backend_to_test=backend_fw,
+        rtol=1e-01,
+        atol=1e-01,
+        frontend=frontend,
+        test_flags=test_flags,
+        fn_tree=fn_tree,
+        on_device=on_device,
+        x=x[0],
+    )
+
+
 # isfinite
 @handle_frontend_test(
     fn_tree="paddle.isfinite",
Repository: ivy-llc/ivy

Issue:
Enhance Error Handling in `ivy\functional\frontends\paddle\vision\transforms.py`
In the file [`ivy\functional\frontends\paddle\vision\transforms.py`](https://github.com/unifyai/ivy/blob/main/ivy/functional/frontends/paddle/vision/transforms.py):
In the following lines:
https://github.com/unifyai/ivy/blob/540534cfc788822012eec764ebab4daca1709da1/ivy/functional/frontends/paddle/vision/transforms.py#L195
https://github.com/unifyai/ivy/blob/540534cfc788822012eec764ebab4daca1709da1/ivy/functional/frontends/paddle/vision/transforms.py#L200
In both the cases a `string` is being raised which is not a good way, I think something like `ValueError` should be raised.


Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/paddle/vision/transforms.py b/ivy/functional/frontends/paddle/vision/transforms.py
--- a/ivy/functional/frontends/paddle/vision/transforms.py
+++ b/ivy/functional/frontends/paddle/vision/transforms.py
@@ -192,12 +192,12 @@ def pad(img, padding, fill=0, padding_mode="constant"):
         elif dim_size == 3:
             trans_padding = ((0, 0), (padding[1], padding[3]), (padding[0], padding[2]))
     else:
-        raise "padding can only be 1D with size 1, 2, 4 only"
+        raise ValueError("padding can only be 1D with size 1, 2, 4 only")
 
     if padding_mode in ["constant", "edge", "reflect", "symmetric"]:
         return ivy.pad(img, trans_padding, mode=padding_mode, constant_values=fill)
     else:
-        raise "Unsupported padding_mode"
+        raise ValueError("Unsupported padding_mode")
 
 
 @with_supported_dtypes(


Test patch:
Repository: ivy-llc/ivy

Issue:
corrcoef
corrcoef instance method to pytorch frontend


Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/torch/tensor.py b/ivy/functional/frontends/torch/tensor.py
--- a/ivy/functional/frontends/torch/tensor.py
+++ b/ivy/functional/frontends/torch/tensor.py
@@ -2163,6 +2163,13 @@ def minimum(self, other, *, out=None):
     def rad2deg(self, *, out=None):
         return torch_frontend.rad2deg(self, out=out)
 
+    @with_supported_dtypes(
+        {"2.1.0 and below": "valid"},
+        "torch",
+    )
+    def corrcoef(self):
+        return torch_frontend.corrcoef(self)
+
     # Method aliases
     absolute, absolute_ = abs, abs_
     clip, clip_ = clamp, clamp_


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_torch/test_tensor.py b/ivy_tests/test_ivy/test_frontends/test_torch/test_tensor.py
--- a/ivy_tests/test_ivy/test_frontends/test_torch/test_tensor.py
+++ b/ivy_tests/test_ivy/test_frontends/test_torch/test_tensor.py
@@ -12718,6 +12718,41 @@ def test_torch_tanh_(
     )
 
 
+# corrcoef
+@handle_frontend_method(
+    class_tree=CLASS_TREE,
+    init_tree="torch.tensor",
+    method_name="corrcoef",
+    dtype_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("valid"),
+    ),
+)
+def test_torch_tensor_corrcoef(
+    dtype_and_x,
+    frontend,
+    backend_fw,
+    frontend_method_data,
+    init_flags,
+    method_flags,
+    on_device,
+):
+    input_dtype, x = dtype_and_x
+    helpers.test_frontend_method(
+        init_input_dtypes=input_dtype,
+        init_all_as_kwargs_np={
+            "data": x[0],
+        },
+        method_input_dtypes=input_dtype,
+        method_all_as_kwargs_np={},
+        frontend_method_data=frontend_method_data,
+        init_flags=init_flags,
+        method_flags=method_flags,
+        frontend=frontend,
+        backend_to_test=backend_fw,
+        on_device=on_device,
+    )
+
+
 # positive
 @handle_frontend_method(
     class_tree=CLASS_TREE,
Repository: ivy-llc/ivy

Issue:
Using PEP 585 annotations without a `from __future__ import annotations` import
In the following line:
https://github.com/unifyai/ivy/blob/ea0eaad440ff7f953c2fb4c621eccfe4a6fb2ecd/ivy/functional/backends/paddle/experimental/statistical.py#L422
It should be `Tuple[Any, ...]:` because we are not using the `from __future__ import annotations` import.
Using `tuple[Any, ...]:` without the `from __future__ import annotations` import will cause runtime errors on Python versions prior to 3.9 and 3.10, respectively.
So, it should be fixed.


Discussion:

Resolving patch:
diff --git a/ivy/functional/backends/paddle/experimental/statistical.py b/ivy/functional/backends/paddle/experimental/statistical.py
--- a/ivy/functional/backends/paddle/experimental/statistical.py
+++ b/ivy/functional/backends/paddle/experimental/statistical.py
@@ -419,7 +419,7 @@ def unravel_index(
     /,
     *,
     out: Optional[paddle.Tensor] = None,
-) -> tuple[Any, ...]:
+) -> Tuple[Any, ...]:
     if indices.ndim == 0:
         indices = indices.unsqueeze(0)
     coord = []


Test patch:
Repository: ivy-llc/ivy

Issue:
Remove useless code/statements that have no effect
In few files there are few useless code/statements that have no effect. So, they should be removed or properly assigned. I detected these using the [`Ruff`](https://docs.astral.sh/ruff/rules/useless-expression/) linter.
Like, in the following places:
https://github.com/unifyai/ivy/blob/8d0ad8159312695e7eba129cfb787a1ad947eddd/ivy/__init__.py#L483
https://github.com/unifyai/ivy/blob/8d0ad8159312695e7eba129cfb787a1ad947eddd/ivy/functional/backends/tensorflow/experimental/elementwise.py#L300
https://github.com/unifyai/ivy/blob/8d0ad8159312695e7eba129cfb787a1ad947eddd/ivy_tests/test_ivy/test_misc/test_factorized_tensor/test_parafac2_tensor.py#L101


Discussion:

Resolving patch:
diff --git a/ivy/__init__.py b/ivy/__init__.py
--- a/ivy/__init__.py
+++ b/ivy/__init__.py
@@ -480,8 +480,7 @@ def is_fully_defined(self):
             shape is not None for shape in self._shape
         )
 
-    property
-
+    @property
     def num_elements(self):
         if not self.is_fully_defined():
             return None
diff --git a/ivy/functional/backends/tensorflow/experimental/elementwise.py b/ivy/functional/backends/tensorflow/experimental/elementwise.py
--- a/ivy/functional/backends/tensorflow/experimental/elementwise.py
+++ b/ivy/functional/backends/tensorflow/experimental/elementwise.py
@@ -297,7 +297,6 @@ def gradient(
     edge_order: int = 1,
 ) -> Union[tf.Tensor, List[tf.Tensor]]:
     # https://github.com/numpy/numpy/blob/v1.24.3/numpy/lib/function_base.py#L969-L1312
-    x.device
     x = tf.experimental.numpy.asanyarray(x)
     N = x.ndim  # number of dimensions
     if axis is None:


Test patch:
diff --git a/ivy_tests/test_ivy/test_misc/test_factorized_tensor/test_parafac2_tensor.py b/ivy_tests/test_ivy/test_misc/test_factorized_tensor/test_parafac2_tensor.py
--- a/ivy_tests/test_ivy/test_misc/test_factorized_tensor/test_parafac2_tensor.py
+++ b/ivy_tests/test_ivy/test_misc/test_factorized_tensor/test_parafac2_tensor.py
@@ -98,7 +98,6 @@ def test_parafac2_to_tensor(weights, factors, projections, true_res):
     projections = [ivy.array(p) for p in projections]
     true_res = ivy.array(true_res)
     res = ivy.Parafac2Tensor.parafac2_to_tensor((weights, factors, projections))
-    (true_res, res)
     assert np.allclose(res, true_res)
Repository: ivy-llc/ivy

Issue:
Remove trailing comma on bare `tuple`
In the file [`ivy\functional\frontends\torch\nn\functional\vision_functions.py`](https://github.com/unifyai/ivy/blob/main/ivy/functional/frontends/torch/nn/functional/vision_functions.py)
in the following function:
https://github.com/unifyai/ivy/blob/567093e891fd4da8a9d5031dbb4c851310b7e182/ivy/functional/frontends/torch/nn/functional/vision_functions.py#L454
There is a un-necessary Trailing comma on bare tuple
https://github.com/unifyai/ivy/blob/567093e891fd4da8a9d5031dbb4c851310b7e182/ivy/functional/frontends/torch/nn/functional/vision_functions.py#L464
The presence of a misplaced comma may cause Python to interpret the value as a tuple, which may lead to unexpected behaviour.
So, it should be removed.

Also, the following test case is repeated
https://github.com/unifyai/ivy/blob/567093e891fd4da8a9d5031dbb4c851310b7e182/ivy_tests/test_ivy/test_misc/test_assertions.py#L839
https://github.com/unifyai/ivy/blob/567093e891fd4da8a9d5031dbb4c851310b7e182/ivy_tests/test_ivy/test_misc/test_assertions.py#L841
This can be removed.


Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/torch/nn/functional/vision_functions.py b/ivy/functional/frontends/torch/nn/functional/vision_functions.py
--- a/ivy/functional/frontends/torch/nn/functional/vision_functions.py
+++ b/ivy/functional/frontends/torch/nn/functional/vision_functions.py
@@ -461,7 +461,7 @@ def pixel_unshuffle(input, downscale_factor):
             f"pixel_unshuffle expects 4D input, but got input with sizes {input_shape}"
         ),
         as_array=False,
-    ),
+    )
 
     b = input_shape[0]
     c = input_shape[1]


Test patch:
diff --git a/ivy_tests/test_ivy/test_misc/test_assertions.py b/ivy_tests/test_ivy/test_misc/test_assertions.py
--- a/ivy_tests/test_ivy/test_misc/test_assertions.py
+++ b/ivy_tests/test_ivy/test_misc/test_assertions.py
@@ -838,7 +838,6 @@ def test_check_true(expression):
         (ivy.array([1, 2, 3]), ivy.array([0, 1, 0], dtype=ivy.int32), 2.0),
         (ivy.array([1, 2, 3]), ivy.array([0, 1, 0], dtype=ivy.int32), 0),
         (ivy.array([1, 2, 3]), ivy.array([0, 1, 0], dtype=ivy.int32), -2),
-        (ivy.array([1, 2, 3]), ivy.array([0, 1, 0], dtype=ivy.int32), 0),
         (ivy.array([1, 2, 3]), ivy.array([0.0, 1.0, 0.0], dtype=ivy.float16), 0),
         (ivy.array([1, 2]), ivy.array([0, 1, 0], dtype=ivy.int32), 0),
         (ivy.array([1, 2, 3]), ivy.array([0, 1], dtype=ivy.int32), 0),
Repository: ivy-llc/ivy

Issue:
Wrong key-word args in function call `torch_frontend.quantile()`
In the following file [`ivy\functional\frontends\torch\tensor.py`](https://github.com/unifyai/ivy/blob/main/ivy/functional/frontends/torch/tensor.py), the arguments are wrongly passed in this function call 
https://github.com/unifyai/ivy/blob/365cfd9b20cc78b009e704bdf0d9de11bb89ba70/ivy/functional/frontends/torch/tensor.py#L1962-L1964
From the definition of the `quantile` function:
https://github.com/unifyai/ivy/blob/365cfd9b20cc78b009e704bdf0d9de11bb89ba70/ivy/functional/frontends/torch/reduction_ops.py#L290
The key-word arguments should be `dim, keepdim` but they were passed as `axis, keepdims`:



Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/torch/tensor.py b/ivy/functional/frontends/torch/tensor.py
--- a/ivy/functional/frontends/torch/tensor.py
+++ b/ivy/functional/frontends/torch/tensor.py
@@ -1960,7 +1960,7 @@ def triu_(self, diagonal=0):
     )
     def quantile(self, q, dim=None, keepdim=False, *, interpolation="linear", out=None):
         return torch_frontend.quantile(
-            self, q, axis=dim, keepdims=keepdim, interpolation=interpolation, out=out
+            self, q, dim=dim, keepdim=keepdim, interpolation=interpolation, out=out
         )
 
     @with_unsupported_dtypes(


Test patch: