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

Issue:
Fix Ivy Failing Test: paddle - elementwise.not_equal



Discussion:

Resolving patch:
diff --git a/ivy/functional/backends/paddle/elementwise.py b/ivy/functional/backends/paddle/elementwise.py
--- a/ivy/functional/backends/paddle/elementwise.py
+++ b/ivy/functional/backends/paddle/elementwise.py
@@ -615,8 +615,8 @@ def negative(
     return paddle.neg(x)
 
 
-@with_supported_dtypes(
-    {"2.6.0 and below": ("float32", "float64", "int32", "int64")},
+@with_unsupported_device_and_dtypes(
+    {"2.6.0 and below": {"cpu": ("bfloat16", "complex128", "float64", "float32")}},
     backend_version,
 )
 def not_equal(


Test patch:
diff --git a/ivy_tests/test_ivy/test_functional/test_core/test_elementwise.py b/ivy_tests/test_ivy/test_functional/test_core/test_elementwise.py
--- a/ivy_tests/test_ivy/test_functional/test_core/test_elementwise.py
+++ b/ivy_tests/test_ivy/test_functional/test_core/test_elementwise.py
@@ -1577,7 +1577,7 @@ def test_negative(*, dtype_and_x, test_flags, backend_fw, fn_name, on_device):
 @handle_test(
     fn_tree="functional.ivy.not_equal",
     dtype_and_x=helpers.dtype_and_values(
-        available_dtypes=helpers.get_dtypes("valid", full=True), num_arrays=2
+        available_dtypes=helpers.get_dtypes("valid", full=False), num_arrays=2
     ),
     test_gradients=st.just(False),
 )
@@ -1592,6 +1592,8 @@ def test_not_equal(*, dtype_and_x, test_flags, backend_fw, fn_name, on_device):
         on_device=on_device,
         x1=x[0],
         x2=x[1],
+        atol_=1e-03,
+        rtol_=1e-03,
     )
Repository: ivy-llc/ivy

Issue:
Fix Frontend Failing Test: torch - tensor.torch.Tensor.unique
#27498 


Discussion:

Resolving patch:
diff --git a/ivy/functional/backends/torch/set.py b/ivy/functional/backends/torch/set.py
--- a/ivy/functional/backends/torch/set.py
+++ b/ivy/functional/backends/torch/set.py
@@ -66,7 +66,7 @@ def unique_all(
             [i[0] for i in sorted(enumerate(values_), key=lambda x: tuple(x[1]))]
         )
     ivy_torch = ivy.current_backend()
-    values = ivy_torch.gather(values, sort_idx, axis=axis)
+    values = values.index_select(dim=axis, index=sort_idx)
     counts = ivy_torch.gather(counts, sort_idx)
     indices = ivy_torch.gather(indices, sort_idx)
     inv_sort_idx = ivy_torch.invert_permutation(sort_idx)
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
@@ -2242,6 +2242,7 @@ def ne_(self, other):
                 "float16",
                 "complex128",
                 "complex64",
+                "bool",
             )
         },
         "torch",


Test patch:
Repository: ivy-llc/ivy

Issue:
 Fix Ivy Failing Test: paddle - linalg.outer



Discussion:

Resolving patch:
diff --git a/ivy/functional/backends/paddle/linear_algebra.py b/ivy/functional/backends/paddle/linear_algebra.py
--- a/ivy/functional/backends/paddle/linear_algebra.py
+++ b/ivy/functional/backends/paddle/linear_algebra.py
@@ -14,6 +14,7 @@
     with_unsupported_device_and_dtypes,
     with_unsupported_dtypes,
     with_supported_dtypes,
+    with_supported_device_and_dtypes,
 )
 from .elementwise import _elementwise_helper
 
@@ -398,6 +399,14 @@ def matrix_transpose(
     return paddle.transpose(x, perm=perm)
 
 
+@with_supported_device_and_dtypes(
+    {
+        "2.6.0 and below": {
+            "cpu": ("int32", "int64", "float64", "complex128" "float32", "complex64")
+        }
+    },
+    backend_version,
+)
 def outer(
     x1: paddle.Tensor, x2: paddle.Tensor, /, *, out: Optional[paddle.Tensor] = None
 ) -> paddle.Tensor:


Test patch:
Repository: ivy-llc/ivy

Issue:
 Fix Ivy Failing Test: paddle - manipulation.expand_dims



Discussion:

Resolving patch:
diff --git a/ivy/functional/backends/paddle/manipulation.py b/ivy/functional/backends/paddle/manipulation.py
--- a/ivy/functional/backends/paddle/manipulation.py
+++ b/ivy/functional/backends/paddle/manipulation.py
@@ -62,6 +62,20 @@ def concat(
     return ret
 
 
+@with_supported_dtypes(
+    {
+        "2.6.0 and below": (
+            "int32",
+            "int64",
+            "float64",
+            "complex128",
+            "float32",
+            "complex64",
+            "bool",
+        )
+    },
+    backend_version,
+)
 def expand_dims(
     x: paddle.Tensor,
     /,


Test patch:
Repository: ivy-llc/ivy

Issue:
 Fix Ivy Failing Test: paddle - manipulation.permute_dims



Discussion:

Resolving patch:
diff --git a/ivy/functional/backends/paddle/manipulation.py b/ivy/functional/backends/paddle/manipulation.py
--- a/ivy/functional/backends/paddle/manipulation.py
+++ b/ivy/functional/backends/paddle/manipulation.py
@@ -94,8 +94,19 @@ def flip(
     return paddle.flip(x, axis)
 
 
-@with_unsupported_dtypes(
-    {"2.6.0 and below": ("uint8", "int8", "int16", "bfloat16")}, backend_version
+@with_supported_dtypes(
+    {
+        "2.6.0 and below": (
+            "int32",
+            "int64",
+            "float64",
+            "complex128",
+            "float32",
+            "complex64",
+            "bool",
+        )
+    },
+    backend_version,
 )
 def permute_dims(
     x: paddle.Tensor,


Test patch:
Repository: ivy-llc/ivy

Issue:
Fix Frontend Failing Test: tensorflow - manipulation.paddle.take_along_axis
To-do List: https://github.com/unifyai/ivy/issues/27499


Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/paddle/manipulation.py b/ivy/functional/frontends/paddle/manipulation.py
--- a/ivy/functional/frontends/paddle/manipulation.py
+++ b/ivy/functional/frontends/paddle/manipulation.py
@@ -198,6 +198,11 @@ def stack(x, axis=0, name=None):
     return ivy.stack(x, axis=axis)
 
 
+@with_supported_dtypes(
+    {"2.6.0 and below": ("float32", "float64")},
+    "paddle",
+)
+@to_ivy_arrays_and_back
 def take_along_axis(arr, indices, axis):
     return ivy.take_along_axis(arr, indices, axis)
 


Test patch:
Repository: ivy-llc/ivy

Issue:
Fix Ivy Failing Test: paddle - activations.relu6



Discussion:

Resolving patch:
diff --git a/ivy/functional/backends/paddle/experimental/activations.py b/ivy/functional/backends/paddle/experimental/activations.py
--- a/ivy/functional/backends/paddle/experimental/activations.py
+++ b/ivy/functional/backends/paddle/experimental/activations.py
@@ -62,7 +62,10 @@ def relu6(
     x: paddle.Tensor, /, *, complex_mode="jax", out: Optional[paddle.Tensor] = None
 ) -> paddle.Tensor:
     if paddle.is_complex(x):
-        return paddle.complex(F.relu6(x.real()), F.relu6(x.imag()))
+        if x.real > 0 and x.real <= 6:
+            return x.astype(x.dtype)
+        else:
+            return paddle_backend.zeros_like(x).astype(x.dtype)
     return F.relu6(x)
 
 


Test patch:
diff --git a/ivy_tests/test_ivy/test_functional/test_experimental/test_nn/test_activations.py b/ivy_tests/test_ivy/test_functional/test_experimental/test_nn/test_activations.py
--- a/ivy_tests/test_ivy/test_functional/test_experimental/test_nn/test_activations.py
+++ b/ivy_tests/test_ivy/test_functional/test_experimental/test_nn/test_activations.py
@@ -218,6 +218,7 @@ def test_prelu(*, dtype_and_x, slope, test_flags, backend_fw, fn_name, on_device
         large_abs_safety_factor=2,
         small_abs_safety_factor=2,
         safety_factor_scale="log",
+        min_value=1e-15,
     ),
     complex_mode=st.sampled_from(["jax", "split", "magnitude"]),
 )
Repository: ivy-llc/ivy

Issue:
Fix Frontend Failing Test: torch - math.tensorflow.math.is_strictly_increasing
To-do List: https://github.com/unifyai/ivy/issues/27498


Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/tensorflow/math.py b/ivy/functional/frontends/tensorflow/math.py
--- a/ivy/functional/frontends/tensorflow/math.py
+++ b/ivy/functional/frontends/tensorflow/math.py
@@ -536,9 +536,11 @@ def is_non_decreasing(x, name="is_non_decreasing"):
 def is_strictly_increasing(x, name="is_strictly_increasing"):
     if ivy.array(x).size < 2:
         return ivy.array(True)
-    if ivy.array(x).size == 2:
-        return ivy.array(x[0] < x[1])
-    return ivy.all(ivy.less(x, ivy.roll(x, -1)))
+    x = ivy.flatten(x)
+    res = ivy.less(x, ivy.roll(x, -1))
+    if res.size >= 2:
+        res[res.size - 1] = True  # The last comparison must be set to true.
+    return ivy.all(res)
 
 
 @to_ivy_arrays_and_back


Test patch:
Repository: ivy-llc/ivy

Issue:
Fix Frontend Failing Test: torch - math.tensorflow.math.argmin
To-do List: https://github.com/unifyai/ivy/issues/27498


Discussion:

Resolving patch:
diff --git a/ivy/functional/backends/torch/searching.py b/ivy/functional/backends/torch/searching.py
--- a/ivy/functional/backends/torch/searching.py
+++ b/ivy/functional/backends/torch/searching.py
@@ -41,7 +41,15 @@ def argmax(
     return ret
 
 
-@with_unsupported_dtypes({"2.2 and below": ("complex",)}, backend_version)
+@with_unsupported_dtypes(
+    {
+        "2.2 and below": (
+            "complex",
+            "bool",
+        )
+    },
+    backend_version,
+)
 def argmin(
     x: torch.Tensor,
     /,


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_tensorflow/test_math.py b/ivy_tests/test_ivy/test_frontends/test_tensorflow/test_math.py
--- a/ivy_tests/test_ivy/test_frontends/test_tensorflow/test_math.py
+++ b/ivy_tests/test_ivy/test_frontends/test_tensorflow/test_math.py
@@ -286,7 +286,7 @@ def test_tensorflow_argmin(
     on_device,
     output_type,
 ):
-    input_dtype, x, axis = dtype_and_x
+    input_dtype, x, axis, *_ = dtype_and_x
     if isinstance(axis, tuple):
         axis = axis[0]
     helpers.test_frontend_function(
Repository: ivy-llc/ivy

Issue:
Fix Frontend Failing Test: tensorflow - math.tensorflow.math.argmin
To-do List: https://github.com/unifyai/ivy/issues/27499


Discussion:

Resolving patch:
diff --git a/ivy/functional/backends/tensorflow/searching.py b/ivy/functional/backends/tensorflow/searching.py
--- a/ivy/functional/backends/tensorflow/searching.py
+++ b/ivy/functional/backends/tensorflow/searching.py
@@ -45,6 +45,7 @@ def argmax(
     return tf.cast(ret, dtype) if dtype is not None else ret
 
 
+@with_unsupported_dtypes({"2.15.0 and below": ("complex",)}, backend_version)
 def argmin(
     x: Union[tf.Tensor, tf.Variable],
     /,
diff --git a/ivy/functional/backends/torch/searching.py b/ivy/functional/backends/torch/searching.py
--- a/ivy/functional/backends/torch/searching.py
+++ b/ivy/functional/backends/torch/searching.py
@@ -13,7 +13,15 @@
 # ------------------ #
 
 
-@with_unsupported_dtypes({"2.2 and below": ("complex",)}, backend_version)
+@with_unsupported_dtypes(
+    {
+        "2.2 and below": (
+            "complex",
+            "bool",
+        )
+    },
+    backend_version,
+)
 def argmax(
     x: torch.Tensor,
     /,
diff --git a/ivy/functional/frontends/tensorflow/math.py b/ivy/functional/frontends/tensorflow/math.py
--- a/ivy/functional/frontends/tensorflow/math.py
+++ b/ivy/functional/frontends/tensorflow/math.py
@@ -193,15 +193,23 @@ def angle(input, name=None):
     return ivy.angle(input)
 
 
+@with_unsupported_dtypes(
+    {"2.15.0 and below": ("complex",)},
+    "tensorflow",
+)
 @to_ivy_arrays_and_back
 def argmax(input, axis, output_type=None, name=None):
     output_type = to_ivy_dtype(output_type)
-    if output_type in ["uint16", "int16", "int32", "int64"]:
+    if output_type in ["int32", "int64"]:
         return ivy.astype(ivy.argmax(input, axis=axis), output_type)
     else:
         return ivy.astype(ivy.argmax(input, axis=axis), "int64")
 
 
+@with_unsupported_dtypes(
+    {"2.15.0 and below": ("complex",)},
+    "tensorflow",
+)
 @to_ivy_arrays_and_back
 def argmin(input, axis=None, output_type="int64", name=None):
     output_type = to_ivy_dtype(output_type)


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_tensorflow/test_math.py b/ivy_tests/test_ivy/test_frontends/test_tensorflow/test_math.py
--- a/ivy_tests/test_ivy/test_frontends/test_tensorflow/test_math.py
+++ b/ivy_tests/test_ivy/test_frontends/test_tensorflow/test_math.py
@@ -238,7 +238,7 @@ def test_tensorflow_angle(
 @handle_frontend_test(
     fn_tree="tensorflow.math.argmax",
     dtype_and_x=_statistical_dtype_values(function="argmax"),
-    output_type=st.sampled_from(["int16", "uint16", "int32", "int64"]),
+    output_type=st.sampled_from(["int32", "int64"]),
     test_with_out=st.just(False),
 )
 def test_tensorflow_argmax(
@@ -251,9 +251,7 @@ def test_tensorflow_argmax(
     on_device,
     output_type,
 ):
-    if backend_fw in ("torch", "paddle"):
-        assume(output_type != "uint16")
-    input_dtype, x, axis = dtype_and_x
+    input_dtype, x, axis, *_ = dtype_and_x
     if isinstance(axis, tuple):
         axis = axis[0]
     helpers.test_frontend_function(
Repository: ivy-llc/ivy

Issue:
fix `complex` dtype support at `paddle backend` in `ivy.maximum`



Discussion:

Resolving patch:
diff --git a/ivy/functional/backends/paddle/elementwise.py b/ivy/functional/backends/paddle/elementwise.py
--- a/ivy/functional/backends/paddle/elementwise.py
+++ b/ivy/functional/backends/paddle/elementwise.py
@@ -1122,7 +1122,13 @@ def maximum(
 ) -> paddle.Tensor:
     x1, x2, ret_dtype = _elementwise_helper(x1, x2)
     if paddle.is_complex(x1):
-        use_where = True
+        real_comparison = paddle.real(x1) > paddle.real(x2)
+        imag_comparison = paddle_backend.logical_and(
+            paddle.real(x1) == paddle.real(x2), paddle.imag(x1) > paddle.imag(x2)
+        )
+        return paddle_backend.where(
+            paddle_backend.logical_or(real_comparison, imag_comparison), x1, x2
+        ).astype(ret_dtype)
     if use_where:
         return paddle_backend.where(
             paddle_backend.greater_equal(x1, x2), x1, x2


Test patch:
diff --git a/ivy_tests/test_ivy/test_functional/test_core/test_elementwise.py b/ivy_tests/test_ivy/test_functional/test_core/test_elementwise.py
--- a/ivy_tests/test_ivy/test_functional/test_core/test_elementwise.py
+++ b/ivy_tests/test_ivy/test_functional/test_core/test_elementwise.py
@@ -69,7 +69,7 @@ def min_max_helper(draw):
     if use_where:
         dtype_and_x = draw(
             helpers.dtype_and_values(
-                available_dtypes=helpers.get_dtypes("numeric"),
+                available_dtypes=helpers.get_dtypes("numeric", full=False),
                 num_arrays=2,
                 small_abs_safety_factor=6,
                 large_abs_safety_factor=6,
@@ -79,7 +79,7 @@ def min_max_helper(draw):
     else:
         dtype_and_x = draw(
             helpers.dtype_and_values(
-                available_dtypes=helpers.get_dtypes("numeric"),
+                available_dtypes=helpers.get_dtypes("numeric", full=False),
                 num_arrays=2,
                 min_value=-1e5,
                 max_value=1e5,
@@ -1438,6 +1438,7 @@ def test_logical_xor(*, dtype_and_x, test_flags, backend_fw, fn_name, on_device)
     fn_tree="functional.ivy.maximum",
     dtype_and_x_and_use_where=min_max_helper(),
     test_gradients=st.just(False),
+    ground_truth_backend="jax",
 )
 def test_maximum(
     *, dtype_and_x_and_use_where, test_flags, backend_fw, fn_name, on_device
Repository: ivy-llc/ivy

Issue:
Fix Frontend Failing Test: jax - math.tensorflow.math.zero_fraction
To-do List: https://github.com/unifyai/ivy/issues/27496


Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/tensorflow/math.py b/ivy/functional/frontends/tensorflow/math.py
--- a/ivy/functional/frontends/tensorflow/math.py
+++ b/ivy/functional/frontends/tensorflow/math.py
@@ -1043,8 +1043,8 @@ def xlogy(x, y, name=None):
 def zero_fraction(value, name="zero_fraction"):
     zero = ivy.zeros(tuple(value.shape), dtype=ivy.float32)
     x = ivy.array(value, dtype=ivy.float32)
-    count_zero = ivy.sum(ivy.equal(x, zero))
-    count_nonzero = ivy.sum(ivy.not_equal(x, zero))
+    count_zero = ivy.sum(ivy.equal(x, zero), dtype=ivy.float32)
+    count_nonzero = ivy.sum(ivy.not_equal(x, zero), dtype=ivy.float32)
     return ivy.divide(count_zero, ivy.add(count_zero, count_nonzero))
 
 


Test patch:
Repository: ivy-llc/ivy

Issue:
fix the `ivy.minimum` support for `complex` dtype at `paddle backend` 



Discussion:

Resolving patch:
diff --git a/ivy/functional/backends/paddle/elementwise.py b/ivy/functional/backends/paddle/elementwise.py
--- a/ivy/functional/backends/paddle/elementwise.py
+++ b/ivy/functional/backends/paddle/elementwise.py
@@ -1098,7 +1098,13 @@ def minimum(
 ) -> paddle.Tensor:
     x1, x2, ret_dtype = _elementwise_helper(x1, x2)
     if paddle.is_complex(x1):
-        use_where = True
+        real_comparison = paddle.real(x1) < paddle.real(x2)
+        imag_comparison = paddle_backend.logical_and(
+            paddle.real(x1) == paddle.real(x2), paddle.imag(x1) < paddle.imag(x2)
+        )
+        return paddle_backend.where(
+            paddle_backend.logical_or(real_comparison, imag_comparison), x1, x2
+        ).astype(ret_dtype)
 
     if use_where:
         return paddle_backend.where(paddle_backend.less_equal(x1, x2), x1, x2).astype(


Test patch:
diff --git a/ivy_tests/test_ivy/test_functional/test_core/test_elementwise.py b/ivy_tests/test_ivy/test_functional/test_core/test_elementwise.py
--- a/ivy_tests/test_ivy/test_functional/test_core/test_elementwise.py
+++ b/ivy_tests/test_ivy/test_functional/test_core/test_elementwise.py
@@ -83,6 +83,8 @@ def min_max_helper(draw):
                 num_arrays=2,
                 min_value=-1e5,
                 max_value=1e5,
+                small_abs_safety_factor=6,
+                large_abs_safety_factor=6,
                 safety_factor_scale="log",
             )
         )
@@ -1182,6 +1184,7 @@ def test_less(*, dtype_and_x, test_flags, backend_fw, fn_name, on_device):
         available_dtypes=helpers.get_dtypes("numeric"), num_arrays=2
     ),
     test_gradients=st.just(False),
+    ground_truth_backend="jax",
 )
 def test_less_equal(*, dtype_and_x, test_flags, backend_fw, fn_name, on_device):
     input_dtype, x = dtype_and_x
@@ -1463,6 +1466,7 @@ def test_maximum(
     fn_tree="functional.ivy.minimum",
     dtype_and_x_and_use_where=min_max_helper(),
     test_gradients=st.just(False),
+    ground_truth_backend="jax",
 )
 def test_minimum(
     *, dtype_and_x_and_use_where, test_flags, backend_fw, fn_name, on_device
Repository: ivy-llc/ivy

Issue:
fix `lint` error in `adaptive_max_pool3d` 



Discussion:

Resolving patch:
diff --git a/ivy/functional/ivy/experimental/layers.py b/ivy/functional/ivy/experimental/layers.py
--- a/ivy/functional/ivy/experimental/layers.py
+++ b/ivy/functional/ivy/experimental/layers.py
@@ -2207,16 +2207,16 @@ def adaptive_max_pool3d(
     Parameters
     ----------
     input
-        Input array. Must have shape (N, C, D_in, H_in, W_in) or (C, D_in, H_in, W_in) where N is
-        the batch dimension, C is the feature dimension, and D_in, H_in, and W_in are the 3
-        spatial dimensions.
+        Input array. Must have shape (N, C, D_in, H_in, W_in) or (C, D_in, H_in, W_in)
+        where N is the batch dimension, C is the feature dimension, and D_in, H_in,
+        and W_in are the 3 spatial dimensions.
     output_size
         Spatial output size.
 
     Returns
     -------
-        The result of the pooling operation. Will have shape (N, C, D_out, H_out, W_out) or
-        (C, D_out, H_out, W_out), where D_out, H_out, W_out = `output_size`
+        The result of the pooling operation. Will have shape (N, C, D_out, H_out, W_out)
+        or (C, D_out, H_out, W_out), where D_out, H_out, W_out = `output_size`
     """
     squeeze = False
     if input.ndim == 4:


Test patch:
Repository: ivy-llc/ivy

Issue:
Fix `doc string` tests for `ivy.hardsilu`



Discussion:

Resolving patch:
diff --git a/ivy/data_classes/container/experimental/activations.py b/ivy/data_classes/container/experimental/activations.py
--- a/ivy/data_classes/container/experimental/activations.py
+++ b/ivy/data_classes/container/experimental/activations.py
@@ -1882,8 +1882,8 @@ def _static_hardsilu(
         >>> y = ivy.Container._static_hardsilu(x)
         >>> print(y)
         {
-            a: ivy.array([-0.20833333, 0.33333333, 0.]),
-            b: ivy.array([0.29166667, 0.66666667, 1.66666667])
+            a: ivy.array([-0.20833333, -0.33333334, 0.]),
+            b: ivy.array([0.29166666, 0.66666669, 1.66666663])
         }
         """
         return ContainerBase.cont_multi_map_in_function(
@@ -1939,8 +1939,8 @@ def hardsilu(
         >>> y = x.hardsilu()
         >>> print(y)
         {
-            a: ivy.array([-0.20833333, 0.33333333, 0.]),
-            b: ivy.array([0.29166667, 0.66666667, 1.66666667])
+            a: ivy.array([-0.20833333, -0.33333334, 0.]),
+            b: ivy.array([0.29166666, 0.66666669, 1.66666663])
         }
         """
         return self._static_hardsilu(
diff --git a/ivy/functional/ivy/experimental/activations.py b/ivy/functional/ivy/experimental/activations.py
--- a/ivy/functional/ivy/experimental/activations.py
+++ b/ivy/functional/ivy/experimental/activations.py
@@ -994,12 +994,12 @@ def hardsilu(
     >>> x = ivy.array([1., 2., 3.])
     >>> y = ivy.hardsilu(x)
     >>> print(y)
-    ivy.array([0.66666667, 1.66666667, 3.])
+    ivy.array([0.66666669, 1.66666663, 3.        ])
     >>> x = ivy.array([-2.1241, 1.4897, 4.4090])
     >>> y = ivy.zeros(3)
     >>> ivy.hardsilu(x, out=y)
     >>> print(y)
-    ivy.array([-0.3101, 1.1147, 4.4090])
+    ivy.array([-0.31008321,  1.1147176 ,  4.40899992])
 
     With :class:`ivy.Container` input:
 
@@ -1007,8 +1007,8 @@ def hardsilu(
     >>> y = ivy.hardsilu(x)
     >>> print(y)
     {
-        a: ivy.array([-0.20833333, 0.33333333, 0.]),
-        b: ivy.array([0.29166667, 0.66666667, 1.66666667])
+        a: ivy.array([-0.20833333, -0.33333334, 0.]),
+        b: ivy.array([0.29166666, 0.66666669, 1.66666663])
     }
     """
     return current_backend(x).hardsilu(x, out=out)


Test patch:
Repository: ivy-llc/ivy

Issue:
Fix Frontend Failing Test: tensorflow - mathematical_functions.jax.numpy.minimum



Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/tensorflow/math.py b/ivy/functional/frontends/tensorflow/math.py
--- a/ivy/functional/frontends/tensorflow/math.py
+++ b/ivy/functional/frontends/tensorflow/math.py
@@ -626,6 +626,7 @@ def maximum(x, y, name=None):
 
 
 @to_ivy_arrays_and_back
+@with_unsupported_dtypes({"2.15.0 and below": ("complex",)}, "tensorflow")
 def minimum(x, y, name=None):
     return ivy.minimum(x, y)
 


Test patch:
Repository: ivy-llc/ivy

Issue:
fix `maximum` at tf `frontend`



Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/tensorflow/math.py b/ivy/functional/frontends/tensorflow/math.py
--- a/ivy/functional/frontends/tensorflow/math.py
+++ b/ivy/functional/frontends/tensorflow/math.py
@@ -621,6 +621,7 @@ def logical_xor(x, y, name="LogicalXor"):
 
 
 @to_ivy_arrays_and_back
+@with_unsupported_dtypes({"2.15.0 and below": ("complex",)}, "tensorflow")
 def maximum(x, y, name=None):
     return ivy.maximum(x, y)
 


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_tensorflow/test_math.py b/ivy_tests/test_ivy/test_frontends/test_tensorflow/test_math.py
--- a/ivy_tests/test_ivy/test_frontends/test_tensorflow/test_math.py
+++ b/ivy_tests/test_ivy/test_frontends/test_tensorflow/test_math.py
@@ -1793,7 +1793,7 @@ def test_tensorflow_logical_xor(
 @handle_frontend_test(
     fn_tree="tensorflow.math.maximum",
     dtype_and_x=helpers.dtype_and_values(
-        available_dtypes=helpers.get_dtypes("float"),
+        available_dtypes=helpers.get_dtypes("numeric"),
         num_arrays=2,
         shared_dtype=True,
     ),