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

Issue:
 tile



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
@@ -29,3 +29,12 @@ def stack(x, axis=0, name=None):
 @to_ivy_arrays_and_back
 def concat(x, axis, name=None):
     return ivy.concat(x, axis=axis)
+
+
+@with_unsupported_dtypes(
+    {"2.4.2 and below": ("int8", "uint8", "int16", "float16")},
+    "paddle",
+)
+@to_ivy_arrays_and_back
+def tile(x, repeat_times, name=None):
+    return ivy.tile(x, repeats=repeat_times)


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_paddle_manipulation.py b/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_paddle_manipulation.py
--- a/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_paddle_manipulation.py
+++ b/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_paddle_manipulation.py
@@ -199,3 +199,50 @@ def test_paddle_concat(
         x=xs,
         axis=unique_idx,
     )
+
+
+# tile
+@st.composite
+def _tile_helper(draw):
+    dtype, x, shape = draw(
+        helpers.dtype_and_values(
+            available_dtypes=helpers.get_dtypes("valid"),
+            min_num_dims=1,
+            max_num_dims=4,
+            min_dim_size=2,
+            max_dim_size=3,
+            ret_shape=True,
+        )
+    )
+    repeats = draw(
+        helpers.list_of_size(
+            x=helpers.ints(min_value=1, max_value=3),
+            size=len(shape),
+        )
+    )
+    return dtype, x, repeats
+
+
+@handle_frontend_test(
+    fn_tree="paddle.tile",
+    dt_x_repeats=_tile_helper(),
+    test_with_out=st.just(False),
+)
+def test_paddle_tile(
+    *,
+    dt_x_repeats,
+    on_device,
+    fn_tree,
+    frontend,
+    test_flags,
+):
+    input_dtypes, x, repeats = dt_x_repeats
+    helpers.test_frontend_function(
+        input_dtypes=input_dtypes,
+        frontend=frontend,
+        test_flags=test_flags,
+        fn_tree=fn_tree,
+        on_device=on_device,
+        x=x[0],
+        repeat_times=repeats,
+    )
Repository: ivy-llc/ivy

Issue:
atan2



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
@@ -170,6 +170,12 @@ def tan(x, name=None):
     return ivy.tan(x)
 
 
+@with_unsupported_dtypes({"2.4.2 and below": ("float16", "bfloat16")}, "paddle")
+@to_ivy_arrays_and_back
+def atan2(x, y, name=None):
+    return ivy.atan2(x, y)
+
+  
 @with_supported_dtypes({"2.5.0 and below": ("float32", "float64")}, "paddle")
 @to_ivy_arrays_and_back
 def square(x, name=None):


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_paddle_math.py b/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_paddle_math.py
--- a/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_paddle_math.py
+++ b/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_paddle_math.py
@@ -750,6 +750,40 @@ def test_paddle_tan(
     )
 
 
+
+# atan2
+@handle_frontend_test(
+    fn_tree="paddle.atan2",
+    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_atan2(
+    *,
+    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,
+        fn_tree=fn_tree,
+        test_flags=test_flags,
+        on_device=on_device,
+        x=x[0],
+        y=x[1],
+    )
+
+
 # sign
 @handle_frontend_test(
     fn_tree="paddle.tensor.math.sign",
Repository: ivy-llc/ivy

Issue:
sign



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
@@ -178,6 +178,12 @@ def square(x, name=None):
 
 @with_unsupported_dtypes({"2.5.0 and below": ("float16", "bfloat16")}, "paddle")
 @to_ivy_arrays_and_back
+def sign(x, name=None):
+    return ivy.sign(x)
+
+
+@with_unsupported_dtypes({"2.4.2 and below": ("float16", "bfloat16")}, "paddle")
+@to_ivy_arrays_and_back
 def neg(x, name=None):
     return ivy.negative(x)
 


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_paddle_math.py b/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_paddle_math.py
--- a/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_paddle_math.py
+++ b/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_paddle_math.py
@@ -675,7 +675,7 @@ def test_paddle_log1p(
 @handle_frontend_test(
     fn_tree="paddle.rad2deg",
     dtype_and_x=helpers.dtype_and_values(
-        available_dtypes=helpers.get_dtypes("valid"),
+        available_dtypes=helpers.get_dtypes("float"),
     ),
 )
 def test_paddle_rad2deg(
@@ -750,6 +750,32 @@ def test_paddle_tan(
     )
 
 
+# sign
+@handle_frontend_test(
+    fn_tree="paddle.tensor.math.sign",
+    dtype_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("float"),
+    ),
+)
+def test_paddle_sign(
+    *,
+    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,
+        x=x[0],
+    )
+
+
 # neg
 @handle_frontend_test(
     fn_tree="paddle.neg",
Repository: ivy-llc/ivy

Issue:
 matrix_power



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
@@ -130,3 +130,10 @@ def bmm(x, y, transpose_x=False, transpose_y=False, name=None):
         raise RuntimeError("input must be 3D matrices")
     x, y = promote_types_of_paddle_inputs(x, y)
     return ivy.matmul(x, y, transpose_a=transpose_x, transpose_b=transpose_y)
+
+
+# matrix_power
+@with_unsupported_dtypes({"2.4.2 and below": ("float16", "bfloat16")}, "paddle")
+@to_ivy_arrays_and_back
+def matrix_power(x, n, name=None):
+    return ivy.matrix_power(x, n)


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_paddle_linalg.py b/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_paddle_linalg.py
--- a/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_paddle_linalg.py
+++ b/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_paddle_linalg.py
@@ -541,3 +541,35 @@ def test_paddle_bmm(
         x=x[0],
         y=x[1],
     )
+
+
+# matrix_power
+@handle_frontend_test(
+    fn_tree="paddle.tensor.linalg.matrix_power",
+    dtype_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("float"),
+        min_value=0,
+        max_value=50,
+        shape=helpers.ints(min_value=2, max_value=8).map(lambda x: tuple([x, x])),
+    ),
+    n=helpers.ints(min_value=1, max_value=8),
+    test_with_out=st.just(False),
+)
+def test_paddle_matrix_power(
+    dtype_and_x,
+    n,
+    frontend,
+    test_flags,
+    fn_tree,
+    on_device,
+):
+    dtype, x = dtype_and_x
+    helpers.test_frontend_function(
+        input_dtypes=dtype,
+        frontend=frontend,
+        test_flags=test_flags,
+        fn_tree=fn_tree,
+        on_device=on_device,
+        x=x[0],
+        n=n,
+    )
Repository: ivy-llc/ivy

Issue:
dropout2d



Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/paddle/nn/functional/common.py b/ivy/functional/frontends/paddle/nn/functional/common.py
--- a/ivy/functional/frontends/paddle/nn/functional/common.py
+++ b/ivy/functional/frontends/paddle/nn/functional/common.py
@@ -1,7 +1,7 @@
 # local
 import ivy
-from ivy.functional.frontends.paddle.func_wrapper import to_ivy_arrays_and_back
 from ivy.func_wrapper import with_supported_dtypes
+from ivy.functional.frontends.paddle.func_wrapper import to_ivy_arrays_and_back
 
 
 @with_supported_dtypes({"2.5.0 and below": ("float32", "float64")}, "paddle")
@@ -25,6 +25,12 @@ def cosine_similarity(x1, x2, *, axis=1, eps=1e-08):
     return cosine
 
 
+@with_supported_dtypes({"2.4.2 and below": ("float32", "float64")}, "paddle")
+@to_ivy_arrays_and_back
+def dropout2d(x, *, p=0.5, training=True, data_format="NCHW", name=None):
+    return ivy.dropout2d(x, p=p, training=training, data_format=data_format)
+
+
 def get_mask(shape, device, prob, seed=None):
     mask = ivy.where(
         ivy.random_uniform(shape=shape, device=device, seed=seed) < prob,


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_paddle/test_nn/test_functional/test_paddle_common.py b/ivy_tests/test_ivy/test_frontends/test_paddle/test_nn/test_functional/test_paddle_common.py
--- a/ivy_tests/test_ivy/test_frontends/test_paddle/test_nn/test_functional/test_paddle_common.py
+++ b/ivy_tests/test_ivy/test_frontends/test_paddle/test_nn/test_functional/test_paddle_common.py
@@ -43,6 +43,52 @@ def test_paddle_cosine_similarity(
     )
 
 
+# Dropout2d
+@handle_frontend_test(
+    fn_tree="paddle.nn.functional.common.dropout2d",
+    d_type_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("valid"),
+        num_arrays=1,
+        shared_dtype=True,
+        min_value=2,
+        max_value=5,
+        min_dim_size=4,
+        shape=(
+            st.integers(min_value=2, max_value=10),
+            4,
+            st.integers(min_value=12, max_value=64),
+            st.integers(min_value=12, max_value=64),
+        ),
+    ),
+    p=st.floats(min_value=0.0, max_value=1.0),
+    training=st.booleans(),
+    data_format=st.sampled_from(["NCHW", "NHWC"]),
+)
+def test_paddle_dropout2d(
+    *,
+    d_type_and_x,
+    p,
+    training,
+    data_format,
+    on_device,
+    fn_tree,
+    frontend,
+    test_flags,
+):
+    dtype, x = d_type_and_x
+    helpers.test_frontend_function(
+        input_dtypes=dtype,
+        frontend=frontend,
+        test_flags=test_flags,
+        fn_tree=fn_tree,
+        on_device=on_device,
+        x=x[0],
+        p=p,
+        training=training,
+        data_format=data_format,
+    )
+
+
 # dropout
 @handle_frontend_test(
     fn_tree="paddle.nn.functional.common.dropout",
Repository: ivy-llc/ivy

Issue:
nested_map 



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
@@ -1055,6 +1055,73 @@ def nested_map(
     ret
         x following the applicable of fn to it's nested leaves, or x itself if x is not
         nested.
+
+    Examples
+    --------
+    With :class:`Tuple` inputs:
+
+    >>> x = ([[1., 2.], [3., 4.]])
+    >>> function = lambda a : a * 2
+    >>> ivy.nested_map(x, function)
+    [[2.0, 4.0], [6.0, 8.0]]
+    >>> print(x)
+    [[2.0, 4.0], [6.0, 8.0]]
+
+    With :code:`Dict` input:
+
+    >>> x = {1 : [1, [2, 3]], 2: (4, 5)}
+    >>> function = lambda a : a + 1
+    >>> ivy.nested_map(x, function)
+    {1 : [2, [3, 4]], 2: (5, 6)}
+    >>> print(x)
+    {1 : [2, [3, 4]], 2: (5, 6)}
+
+    With :code:`List` inputs:
+
+    >>> x = [['a', 'b', 'c'],
+    ...      ['d', 'e', 'f'],
+    ...      ['g', ['h', 'i']]]
+    >>> function = lambda a: a + 'H'
+    >>> ivy.nested_map(x, function)
+    [['aH','bH','cH'],['dH','eH','fH'],['gH',['hH','iH']]]
+    >>> print(x)
+    [['aH','bH','cH'],['dH','eH','fH'],['gH',['hH','iH']]]
+
+    With :class:`ivy.Container` input:
+
+    >>> x = ivy.Container(
+    ...   a=ivy.array([[1, 2, 3], [9, 8, 7]]) , b=ivy.array([[4, 5, 6], [12, 13, 14]])
+    ... )
+    >>> function = lambda a : a  + 1
+    >>> ivy.nested_map(x, function)
+    {
+        a: ivy.array([[2, 3, 4],
+                      [10, 9, 8]]),
+        b: ivy.array([[5, 6, 7],
+                      [13, 14, 15]])
+    }
+    >>> print(x)
+    {
+        a: ivy.array([[2, 3, 4],
+                      [10, 9, 8]]),
+        b: ivy.array([[5, 6, 7],
+                      [13, 14, 15]])
+    }
+
+    >>> nest = ([1, 2], [3, 4], [5, 6], {"a": 1, "b": 2, "c": 3})
+    >>> function = lambda a :  a * 2
+    >>> ivy.nested_map(nest, function,  to_ignore=list)
+    ([1, 2, 1, 2], [3, 4, 3, 4], [5, 6, 5, 6], {'a': 2, 'b': 4, 'c': 6})
+
+    >>> nest = [[1, 2], [3, [4, 5]], [[6], [7, 8, [9, 10]]]]
+    >>> function = lambda a :  a * 2
+    >>> ivy.nested_map(nest, function, max_depth = 3)
+    [[2, 4], [6, [8, 10]], [[12], [14, 16, [9, 10]]]]
+
+    >>> nest = ([23, 25, 1337], [63, 98, 6])
+    >>> function = lambda a :  a + 1
+    >>> ivy.nested_map(nest, function, to_mutable = True)
+    [[24, 25, 1338], [64, 99, 7]]
     """
     to_ignore = ivy.default(to_ignore, ())
     extra_nest_types = ivy.default(extra_nest_types, ())


Test patch:
Repository: ivy-llc/ivy

Issue:
bitwise_not_



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
@@ -908,6 +908,10 @@ def unbind(self, dim=0):
     def remainder(self, other, *, out=None):
         return torch_frontend.remainder(self, other, out=out)
 
+    def bitwise_not_(self):
+        self.ivy_array = self.bitwise_not().ivy_array
+        return self
+
     def bitwise_and_(self, other):
         self.ivy_array = self.bitwise_and(other).ivy_array
         return self


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
@@ -6993,6 +6993,40 @@ def test_torch_instance_atan2_(
     )
 
 
+# bitwise_not_
+@handle_frontend_method(
+    class_tree=CLASS_TREE,
+    init_tree="torch.tensor",
+    method_name="bitwise_not_",
+    dtype_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("integer"),
+        num_arrays=2,
+    ),
+)
+def test_torch_instance_bitwise_not_(
+    dtype_and_x,
+    frontend_method_data,
+    init_flags,
+    method_flags,
+    frontend,
+    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,
+        frontend_method_data=frontend_method_data,
+        init_flags=init_flags,
+        method_flags=method_flags,
+        method_all_as_kwargs_np={},
+        frontend=frontend,
+        on_device=on_device,
+    )
+
+
 # bitwise_and_
 @handle_frontend_method(
     class_tree=CLASS_TREE,
Repository: ivy-llc/ivy

Issue:
addmv



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
@@ -134,6 +134,10 @@ def addmm_(self, mat1, mat2, *, beta=1, alpha=1):
         self.ivy_array = self.addmm(mat1, mat2, beta=beta, alpha=alpha).ivy_array
         return self
 
+    @with_unsupported_dtypes({"2.0.1 and below": ("float16",)}, "torch")
+    def addmv(self, mat, vec, *, beta=1, alpha=1):
+        return torch_frontend.addmv(self, mat, vec, beta=beta, alpha=alpha)
+
     @with_unsupported_dtypes({"2.0.1 and below": ("float16",)}, "torch")
     def addbmm(self, batch1, batch2, *, beta=1, alpha=1):
         return torch_frontend.addbmm(self, batch1, batch2, beta=beta, alpha=alpha)


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
@@ -29,6 +29,7 @@
 from ivy_tests.test_ivy.test_frontends.test_torch.test_blas_and_lapack_ops import (
     _get_dtype_and_3dbatch_matrices,
     _get_dtype_input_and_matrices,
+    _get_dtype_input_and_mat_vec,
 )
 from ivy.functional.frontends.torch import Tensor
 from ivy_tests.test_ivy.helpers import handle_frontend_method
@@ -464,6 +465,59 @@ def test_torch_instance_addmm_(
     )
 
 
+# addmv
+@handle_frontend_method(
+    class_tree=CLASS_TREE,
+    init_tree="torch.tensor",
+    method_name="addmv",
+    dtype_and_matrices=_get_dtype_input_and_mat_vec(with_input=True),
+    beta=st.floats(
+        min_value=-5,
+        max_value=5,
+        allow_nan=False,
+        allow_subnormal=False,
+        allow_infinity=False,
+    ),
+    alpha=st.floats(
+        min_value=-5,
+        max_value=5,
+        allow_nan=False,
+        allow_subnormal=False,
+        allow_infinity=False,
+    ),
+)
+def test_torch_instance_addmv(
+    dtype_and_matrices,
+    beta,
+    alpha,
+    frontend,
+    frontend_method_data,
+    init_flags,
+    method_flags,
+    on_device,
+):
+    input_dtype, x, mat, vec = dtype_and_matrices
+    helpers.test_frontend_method(
+        init_input_dtypes=input_dtype,
+        init_all_as_kwargs_np={
+            "data": x,
+        },
+        method_input_dtypes=input_dtype,
+        method_all_as_kwargs_np={
+            "mat": mat,
+            "vec": vec,
+            "beta": beta,
+            "alpha": alpha,
+        },
+        frontend_method_data=frontend_method_data,
+        init_flags=init_flags,
+        method_flags=method_flags,
+        frontend=frontend,
+        atol_=1e-02,
+        on_device=on_device,
+    )
+
+
 # addbmm
 @handle_frontend_method(
     class_tree=CLASS_TREE,
Repository: ivy-llc/ivy

Issue:
Fix tensor.test_torch_instance_new_zeros
|   |   |
|---|---|
|tensorflow|<a href="https://github.com/unifyai/ivy/actions/runs/5740357384"><img src=https://img.shields.io/badge/-failure-red></a>
|jax|<a href="https://github.com/unifyai/ivy/actions/runs/5740357384"><img src=https://img.shields.io/badge/-failure-red></a>
|numpy|<a href="https://github.com/unifyai/ivy/actions/runs/5740357384"><img src=https://img.shields.io/badge/-failure-red></a>
|torch|<a href="https://github.com/unifyai/ivy/actions/runs/5740357384"><img src=https://img.shields.io/badge/-failure-red></a>
|paddle|<a href="https://github.com/unifyai/ivy/actions/runs/5740357384"><img src=https://img.shields.io/badge/-failure-red></a>



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
@@ -402,13 +402,15 @@ def not_equal(self, other, *, out=None):
     def equal(self, other):
         return torch_frontend.equal(self, other)
 
-    def new_zeros(self, *size, dtype=None, device=None, requires_grad=False):
+    def new_zeros(
+        self, size, *, dtype=None, device=None, requires_grad=False, layout=None
+    ):
         if isinstance(size[0], tuple):
             return torch_frontend.zeros(
-                size[0], dtype=dtype, device=device, requires_grad=requires_grad
+                size=size[0], dtype=dtype, device=device, requires_grad=requires_grad
             )
         return torch_frontend.zeros(
-            size, dtype=dtype, device=device, requires_grad=requires_grad
+            size=size, dtype=dtype, device=device, requires_grad=requires_grad
         )
 
     def to(self, *args, **kwargs):


Test patch:
Repository: ivy-llc/ivy

Issue:
sign_



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
@@ -1308,6 +1308,11 @@ def addcmul_(self, tensor1, tensor2, *, value=1):
     def sign(self):
         return torch_frontend.sign(self._ivy_array)
 
+    @with_unsupported_dtypes({"2.0.1 and below": sign_decorator_dtypes}, "torch")
+    def sign_(self):
+        self.ivy_array = self.sign().ivy_array
+        return self
+
     @numpy_to_torch_style_args
     def std(self, dim=None, unbiased=True, keepdim=False, *, out=None):
         return torch_frontend.std(


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
@@ -8329,6 +8329,37 @@ def test_torch_instance_sign(
     )
 
 
+# sign_
+@handle_frontend_method(
+    class_tree=CLASS_TREE,
+    init_tree="torch.tensor",
+    method_name="sign_",
+    dtype_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("valid"),
+    ),
+)
+def test_torch_instance_sign_(
+    dtype_x,
+    frontend,
+    frontend_method_data,
+    init_flags,
+    method_flags,
+    on_device,
+):
+    input_dtype, x = dtype_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,
+        on_device=on_device,
+        frontend=frontend,
+    )
+
+
 # std
 @handle_frontend_method(
     class_tree=CLASS_TREE,
Repository: ivy-llc/ivy

Issue:
isinf
I will take care of the 'isinf' instance method of the 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
@@ -940,6 +940,9 @@ def fix_(self):
         self.ivy_array = self.fix().ivy_array
         return self
 
+    def isinf(self):
+        return torch_frontend.isinf(self._ivy_array)
+
     def is_complex(self):
         return torch_frontend.is_complex(self._ivy_array)
 


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
@@ -8599,6 +8599,37 @@ def test_torch_instance_norm(
     )
 
 
+# isinf
+@handle_frontend_method(
+    class_tree=CLASS_TREE,
+    init_tree="torch.tensor",
+    method_name="isinf",
+    dtype_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("valid"),
+    ),
+)
+def test_torch_instance_isinf(
+    dtype_and_x,
+    frontend_method_data,
+    init_flags,
+    method_flags,
+    frontend,
+    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,
+        on_device=on_device,
+    )
+
+
 # is_complex
 @handle_frontend_method(
     class_tree=CLASS_TREE,