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

Issue:
numpy.column_stack



Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/numpy/manipulation_routines/joining_arrays.py b/ivy/functional/frontends/numpy/manipulation_routines/joining_arrays.py
--- a/ivy/functional/frontends/numpy/manipulation_routines/joining_arrays.py
+++ b/ivy/functional/frontends/numpy/manipulation_routines/joining_arrays.py
@@ -10,6 +10,16 @@
 import ivy.functional.frontends.numpy as np_frontend
 
 
+@to_ivy_arrays_and_back
+def column_stack(tup):
+    out_dtype = ivy.dtype(tup[0])
+    for i in tup:
+        out_dtype = ivy.as_ivy_dtype(
+            np_frontend.promote_numpy_dtypes(i.dtype, out_dtype)
+        )
+    return ivy.column_stack(tup)
+
+
 @handle_numpy_out
 @handle_numpy_dtype
 @to_ivy_arrays_and_back


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_numpy/test_manipulation_routines/test_joining_arrays.py b/ivy_tests/test_ivy/test_frontends/test_numpy/test_manipulation_routines/test_joining_arrays.py
--- a/ivy_tests/test_ivy/test_frontends/test_numpy/test_manipulation_routines/test_joining_arrays.py
+++ b/ivy_tests/test_ivy/test_frontends/test_numpy/test_manipulation_routines/test_joining_arrays.py
@@ -41,6 +41,38 @@ def _arrays_idx_n_dtypes(draw):
 # ------------ #
 
 
+# column_stack
+@handle_frontend_test(
+    fn_tree="numpy.column_stack",
+    dtype_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("valid"),
+        shared_dtype=True,
+        num_arrays=helpers.ints(min_value=2, max_value=10),
+        shape=helpers.get_shape(
+            min_num_dims=1,
+        ),
+    ),
+)
+def test_numpy_column_stack(
+    dtype_and_x,
+    frontend,
+    test_flags,
+    fn_tree,
+    backend_fw,
+    on_device,
+):
+    input_dtype, xs = 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,
+        tup=xs,
+    )
+
+
 # concat
 @handle_frontend_test(
     fn_tree="numpy.concatenate",
Repository: ivy-llc/ivy

Issue:
 __invert__



Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/paddle/tensor/tensor.py b/ivy/functional/frontends/paddle/tensor/tensor.py
--- a/ivy/functional/frontends/paddle/tensor/tensor.py
+++ b/ivy/functional/frontends/paddle/tensor/tensor.py
@@ -217,6 +217,9 @@ def __float__(self):
     def __xor__(self, y, /, name=None):
         return paddle_frontend.logic.bitwise_xor(self, y)
 
+    def __invert__(self, out=None, name=None):
+        return paddle_frontend.logic.bitwise_not(self)
+
     def __len__(self):
         return len(self._ivy_array)
 


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_tensor.py b/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_tensor.py
--- a/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_tensor.py
+++ b/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_tensor.py
@@ -832,6 +832,42 @@ def test_paddle__int__(
     )
 
 
+# invert
+@handle_frontend_method(
+    class_tree=CLASS_TREE,
+    init_tree="paddle.to_tensor",
+    method_name="__invert__",
+    dtype_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("numeric"),
+        max_num_dims=0,
+    ),
+)
+def test_paddle__invert__(
+    dtype_and_x,
+    frontend_method_data,
+    init_flags,
+    method_flags,
+    backend_fw,
+    frontend,
+    on_device,
+):
+    input_dtypes, xs = dtype_and_x
+    helpers.test_frontend_method(
+        init_input_dtypes=input_dtypes,
+        backend_to_test=backend_fw,
+        method_input_dtypes=input_dtypes,
+        init_all_as_kwargs_np={
+            "object": xs[0],
+        },
+        method_all_as_kwargs_np={},
+        frontend=frontend,
+        frontend_method_data=frontend_method_data,
+        init_flags=init_flags,
+        method_flags=method_flags,
+        on_device=on_device,
+    )
+
+
 # __len__
 @handle_frontend_method(
     class_tree=CLASS_TREE,
Repository: ivy-llc/ivy

Issue:
Wrong key-word argument `beta` in `smooth_l1_loss` function call
In the following function call,
https://github.com/unifyai/ivy/blob/d3a7737d0b3b0957c5379b6adcb8b7041cbf2d15/ivy/functional/backends/paddle/experimental/losses.py#L68-L70

The key-word argument `beta` is used, it should be `delta` 
https://github.com/PaddlePaddle/Paddle/blob/48c21b50fa2ecce7e48753da9b3e9b1cdac60e36/python/paddle/nn/functional/loss.py#L1049


Discussion:

Resolving patch:
diff --git a/ivy/functional/backends/paddle/experimental/losses.py b/ivy/functional/backends/paddle/experimental/losses.py
--- a/ivy/functional/backends/paddle/experimental/losses.py
+++ b/ivy/functional/backends/paddle/experimental/losses.py
@@ -66,7 +66,7 @@ def smooth_l1_loss(
     reduction: Optional[str] = "mean",
 ) -> paddle.Tensor:
     return paddle.nn.functional.smooth_l1_loss(
-        input, target, reduction=reduction, beta=beta
+        input, target, reduction=reduction, delta=beta
     )
 
 


Test patch:
Repository: ivy-llc/ivy

Issue:
Unexpected argument `input` in function call `paddle.concat()`
In the following function call,
https://github.com/unifyai/ivy/blob/d3a7737d0b3b0957c5379b6adcb8b7041cbf2d15/ivy/functional/backends/paddle/set.py#L137-L140
The argument `input` is passed but if we see the actual definition of `paddle.concat()` there is no argument with the name of `input`. 
The correct argument is `x` but it is not mandatory to pass it as a key-word argument.
https://github.com/PaddlePaddle/Paddle/blob/48c21b50fa2ecce7e48753da9b3e9b1cdac60e36/python/paddle/tensor/manipulation.py#L1206

The correct way to call the function is:
https://github.com/unifyai/ivy/blob/d3a7737d0b3b0957c5379b6adcb8b7041cbf2d15/ivy/functional/backends/paddle/set.py#L100-L102
https://github.com/unifyai/ivy/blob/d3a7737d0b3b0957c5379b6adcb8b7041cbf2d15/ivy/functional/backends/paddle/set.py#L103-L105 



Discussion:

Resolving patch:
diff --git a/ivy/functional/backends/paddle/set.py b/ivy/functional/backends/paddle/set.py
--- a/ivy/functional/backends/paddle/set.py
+++ b/ivy/functional/backends/paddle/set.py
@@ -135,7 +135,7 @@ def unique_inverse(
             x.dtype
         )
         unique = paddle.concat(
-            input=[unique.astype(x.dtype), paddle.reshape(unique_nan, [nan_count])],
+            [unique.astype(x.dtype), paddle.reshape(unique_nan, [nan_count])],
             axis=-1,
         )
     inverse_val = paddle.reshape(inverse_val, shape=x.shape)


Test patch:
Repository: ivy-llc/ivy

Issue:
No value for argument `val` in function call `ivy.inplace_update()`
In the following function call, the argument for `val` is not passed.
https://github.com/unifyai/ivy/blob/3f28537e0a0783ebcd68e2c11e9c6a30ed4b4d17/ivy/functional/backends/jax/experimental/manipulation.py#L462-L465
From the actual definition of the `inplace_update()` function, 2 arguments should be passed (`x, and val`)
https://github.com/unifyai/ivy/blob/3f28537e0a0783ebcd68e2c11e9c6a30ed4b4d17/ivy/functional/ivy/general.py#L2954-L2961



Discussion:

Resolving patch:
diff --git a/ivy/functional/backends/jax/experimental/manipulation.py b/ivy/functional/backends/jax/experimental/manipulation.py
--- a/ivy/functional/backends/jax/experimental/manipulation.py
+++ b/ivy/functional/backends/jax/experimental/manipulation.py
@@ -462,7 +462,7 @@ def take(
     # clip, wrap, fill
     ret = jnp.take(x, indices, axis=axis, mode=mode, fill_value=fill_value)
     if ivy.exists(out):
-        ivy.inplace_update(out)
+        ivy.inplace_update(out, ret)
     return ret
 
 


Test patch:
Repository: ivy-llc/ivy

Issue:
__rtruediv__



Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/paddle/tensor/tensor.py b/ivy/functional/frontends/paddle/tensor/tensor.py
--- a/ivy/functional/frontends/paddle/tensor/tensor.py
+++ b/ivy/functional/frontends/paddle/tensor/tensor.py
@@ -233,6 +233,13 @@ def __neg__(self):
     def __rdiv__(self, y, /, name=None):
         return paddle_frontend.divide(y, self)
 
+    @with_unsupported_dtypes(
+        {"2.5.2 and below": ("bool", "unsigned", "int8", "float16", "bfloat16")},
+        "paddle",
+    )
+    def __rtruediv__(self, y, /, name=None):
+        return paddle_frontend.divide(y, self)
+
     @with_unsupported_dtypes(
         {"2.5.2 and below": ("bool", "unsigned", "int8", "float16", "bfloat16")},
         "paddle",


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_tensor.py b/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_tensor.py
--- a/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_tensor.py
+++ b/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_tensor.py
@@ -1287,6 +1287,49 @@ def test_paddle__rsub__(
     )
 
 
+@handle_frontend_method(
+    class_tree=CLASS_TREE,
+    init_tree="paddle.to_tensor",
+    method_name="__rtruediv__",
+    dtype_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("numeric"),
+        num_arrays=2,
+        shared_dtype=True,
+        large_abs_safety_factor=10,
+        small_abs_safety_factor=10,
+        safety_factor_scale="log",
+    ),
+)
+def test_paddle__rtruediv__(
+    dtype_and_x,
+    frontend,
+    frontend_method_data,
+    init_flags,
+    method_flags,
+    on_device,
+    backend_fw,
+):
+    input_dtype, x = dtype_and_x
+    assume(not np.any(np.isclose(x[0], 0)))
+    assume(not np.any(np.isclose(x[1], 0)))
+    helpers.test_frontend_method(
+        init_input_dtypes=input_dtype,
+        backend_to_test=backend_fw,
+        init_all_as_kwargs_np={
+            "value": x[0],
+        },
+        method_input_dtypes=input_dtype,
+        method_all_as_kwargs_np={
+            "y": x[1],
+        },
+        frontend=frontend,
+        frontend_method_data=frontend_method_data,
+        init_flags=init_flags,
+        method_flags=method_flags,
+        on_device=on_device,
+    )
+
+
 # __xor__
 @handle_frontend_method(
     class_tree=CLASS_TREE,
Repository: ivy-llc/ivy

Issue:
Fix Ivy Failing Test: jax - activations.relu
ToDo list : #27501


Discussion:

Resolving patch:
diff --git a/ivy/functional/backends/jax/activations.py b/ivy/functional/backends/jax/activations.py
--- a/ivy/functional/backends/jax/activations.py
+++ b/ivy/functional/backends/jax/activations.py
@@ -37,7 +37,7 @@ def leaky_relu(
 def relu(
     x: JaxArray, /, *, complex_mode="jax", out: Optional[JaxArray] = None
 ) -> JaxArray:
-    return jnp.maximum(x, 0)
+    return jax.nn.relu(x)
 
 
 def sigmoid(


Test patch:
Repository: ivy-llc/ivy

Issue:
Fix Ivy Failing Test: paddle - elementwise.tanh
ToDo list #27501


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
@@ -619,7 +619,7 @@ def not_equal(
 
 
 @with_supported_device_and_dtypes(
-    {"2.5.2 and below": {"cpu": ("bfloat16", "float32", "float64", "complex")}},
+    {"2.5.2 and below": {"cpu": ("float32", "float64", "complex")}},
     backend_version,
 )
 def tanh(x: paddle.Tensor, /, *, out: Optional[paddle.Tensor] = None) -> paddle.Tensor:


Test patch:
Repository: ivy-llc/ivy

Issue:
Fix Ivy Failing Test: jax - elementwise.log
Todo list : #27501


Discussion:

Resolving patch:
diff --git a/ivy/functional/backends/tensorflow/elementwise.py b/ivy/functional/backends/tensorflow/elementwise.py
--- a/ivy/functional/backends/tensorflow/elementwise.py
+++ b/ivy/functional/backends/tensorflow/elementwise.py
@@ -431,6 +431,7 @@ def less_equal(
     return tf.math.less_equal(x1, x2)
 
 
+@with_unsupported_dtypes({"2.15.0 and below": ("float16", "bfloat16")}, backend_version)
 def log(
     x: Union[tf.Tensor, tf.Variable],
     /,


Test patch:
Repository: ivy-llc/ivy

Issue:
Missing argument `ret_idxs` in function call `_rebuild_flattened_containers()`
In the following function call, the argument for `ret_idxs` is not passed.
https://github.com/unifyai/ivy/blob/1d32057c5a2508e60b66eecc98e555c16ced07ea/ivy/functional/backends/mxnet/experimental/gradients.py#L48-L50

From the actual definition of the `_rebuild_flattened_containers()` function, 2 arguments (`outputs`, `ret_idxs`) must be passed.
https://github.com/unifyai/ivy/blob/1d32057c5a2508e60b66eecc98e555c16ced07ea/ivy/functional/ivy/gradients.py#L298

The implementation is similar to this one
https://github.com/unifyai/ivy/blob/1d32057c5a2508e60b66eecc98e555c16ced07ea/ivy/functional/backends/tensorflow/experimental/gradients.py#L58-L60


Discussion:

Resolving patch:
diff --git a/ivy/functional/backends/mxnet/experimental/gradients.py b/ivy/functional/backends/mxnet/experimental/gradients.py
--- a/ivy/functional/backends/mxnet/experimental/gradients.py
+++ b/ivy/functional/backends/mxnet/experimental/gradients.py
@@ -46,7 +46,7 @@ def vjpfun(x_in):
         )
 
         return _rebuild_flattened_containers(
-            ivy.to_ivy(grads, nested=True, include_derived=True)
+            ivy.to_ivy(grads, nested=True, include_derived=True), ret_idxs
         )
 
     return (ivy.to_ivy(primals_out, nested=True, include_derived=True), vjpfun)


Test patch:
Repository: ivy-llc/ivy

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



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
@@ -841,8 +841,9 @@ def trapz(
     return ret
 
 
-@with_unsupported_device_and_dtypes(
-    {"2.5.2 and below": {"cpu": ("float16",)}}, backend_version
+@with_supported_device_and_dtypes(
+    {"2.5.2 and below": {"cpu": ("float32", "float64", "int32", "int64", "complex")}},
+    backend_version,
 )
 def abs(
     x: Union[float, paddle.Tensor],


Test patch:
Repository: ivy-llc/ivy

Issue:
 Fix Ivy Failing Test: torch - dtype.astype



Discussion:

Resolving patch:
diff --git a/ivy/functional/backends/torch/data_type.py b/ivy/functional/backends/torch/data_type.py
--- a/ivy/functional/backends/torch/data_type.py
+++ b/ivy/functional/backends/torch/data_type.py
@@ -72,6 +72,7 @@ def smallest_normal(self):
 # -------------------#
 
 
+@with_unsupported_dtypes({"2.1.2 and below": ("bfloat16", "float16")}, backend_version)
 def astype(
     x: torch.Tensor,
     dtype: torch.dtype,


Test patch:
Repository: ivy-llc/ivy

Issue:
Fix Ivy Failing Test: paddle - elementwise.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
@@ -120,8 +120,9 @@ def isinf(
     return paddle.zeros(shape=x.shape, dtype=bool)
 
 
-@with_supported_dtypes(
-    {"2.6.0 and below": ("float32", "float64", "int32", "int64")}, backend_version
+@with_unsupported_dtypes(
+    {"2.6.0 and below": ("bfloat16",)},
+    backend_version,
 )
 def equal(
     x1: Union[float, paddle.Tensor],
@@ -131,16 +132,11 @@ def equal(
     out: Optional[paddle.Tensor] = None,
 ) -> paddle.Tensor:
     x1, x2, ret_dtype = _elementwise_helper(x1, x2)
-    diff = paddle_backend.subtract(x1, x2)
-    ret = paddle_backend.logical_and(
-        paddle_backend.less_equal(diff, 0), paddle_backend.greater_equal(diff, 0)
-    )
-    # ret result is sufficient for all cases except where the value is +/-INF of NaN
-    return paddle_backend.where(
-        paddle_backend.isnan(diff),
-        ~paddle_backend.logical_or(paddle_backend.isnan(x1), paddle_backend.isnan(x2)),
-        ret,
-    )
+    if paddle.is_complex(x1):
+        real = paddle.equal(x1.real(), x2.real())
+        imag = paddle.equal(x1.imag(), x2.imag())
+        return paddle_backend.logical_and(real, imag)
+    return paddle.equal(x1, x2)
 
 
 @with_supported_dtypes(


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
@@ -687,7 +687,7 @@ def test_divide(*, dtype_and_x, test_flags, backend_fw, fn_name, on_device):
 @handle_test(
     fn_tree="functional.ivy.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),
 )
Repository: ivy-llc/ivy

Issue:
No value for argument `shape` in `reshape()` function call 
In the following function call, the `shape` argument is not passed.
https://github.com/unifyai/ivy/blob/ef2c6d04e7c6c76535ff159011dbfd8b1f7f3704/ivy/functional/frontends/paddle/tensor/tensor.py#L305
From the if-else conditions above, this function call happens when both `shape` and `args` are `None`.
This is similar to this issue (https://github.com/unifyai/ivy/issues/27351)


Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/paddle/tensor/tensor.py b/ivy/functional/frontends/paddle/tensor/tensor.py
--- a/ivy/functional/frontends/paddle/tensor/tensor.py
+++ b/ivy/functional/frontends/paddle/tensor/tensor.py
@@ -301,9 +301,8 @@ def reshape_(self, *args, shape=None):
                     self._ivy_array, args
                 ).ivy_array
                 return self
-
-        self.ivy_array = paddle_frontend.reshape(self._ivy_array).ivy_array
-        return self
+        else:
+            raise ValueError("reshape_() got no values for argument 'shape'")
 
     def dim(self):
         return self.ivy_array.ndim


Test patch:
Repository: ivy-llc/ivy

Issue:
Fix Ivy Failing Test: jax, torch - elementwise.tan



Discussion:

Resolving patch:
diff --git a/ivy/functional/backends/jax/elementwise.py b/ivy/functional/backends/jax/elementwise.py
--- a/ivy/functional/backends/jax/elementwise.py
+++ b/ivy/functional/backends/jax/elementwise.py
@@ -514,6 +514,9 @@ def trapz(
     return jnp.trapz(y, x=x, dx=dx, axis=axis)
 
 
+@with_unsupported_dtypes(
+    {"0.4.23 and below": ("complex", "float16", "bfloat16")}, backend_version
+)
 def tan(x: JaxArray, /, *, out: Optional[JaxArray] = None) -> JaxArray:
     return jnp.tan(x)
 
diff --git a/ivy/functional/backends/torch/elementwise.py b/ivy/functional/backends/torch/elementwise.py
--- a/ivy/functional/backends/torch/elementwise.py
+++ b/ivy/functional/backends/torch/elementwise.py
@@ -723,7 +723,7 @@ def logaddexp2(
 logaddexp2.support_native_out = True
 
 
-@with_unsupported_dtypes({"2.1.2 and below": ("float16",)}, backend_version)
+@with_unsupported_dtypes({"2.1.2 and below": ("float16", "bfloat16")}, backend_version)
 @handle_numpy_arrays_in_specific_backend
 def tan(x: torch.Tensor, /, *, out: Optional[torch.Tensor] = None) -> torch.Tensor:
     x = _cast_for_unary_op(x)


Test patch:
Repository: ivy-llc/ivy

Issue:
Fix Ivy Failing Test: jax - elementwise.atanh



Discussion:

Resolving patch:
diff --git a/ivy/functional/backends/jax/elementwise.py b/ivy/functional/backends/jax/elementwise.py
--- a/ivy/functional/backends/jax/elementwise.py
+++ b/ivy/functional/backends/jax/elementwise.py
@@ -68,6 +68,7 @@ def atan2(x1: JaxArray, x2: JaxArray, /, *, out: Optional[JaxArray] = None) -> J
     return jnp.arctan2(x1, x2)
 
 
+@with_unsupported_dtypes({"0.4.23 and below": ("complex",)}, backend_version)
 def atanh(x: JaxArray, /, *, out: Optional[JaxArray] = None) -> JaxArray:
     return jnp.arctanh(x)
 


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
@@ -390,7 +390,9 @@ def test_atan2(*, dtype_and_x, test_flags, backend_fw, fn_name, on_device):
 @handle_test(
     fn_tree="functional.ivy.atanh",
     dtype_and_x=helpers.dtype_and_values(
-        available_dtypes=helpers.get_dtypes("float_and_complex")
+        min_value=1e-30,
+        max_value=1e30,
+        available_dtypes=helpers.get_dtypes("float_and_complex"),
     ),
 )
 def test_atanh(*, dtype_and_x, test_flags, backend_fw, fn_name, on_device):
Repository: ivy-llc/ivy

Issue:
Wrong key-word argument `dim` in function call `ivy.ifftn()`
In the following function call, the key-word argument `dim` is used:
https://github.com/unifyai/ivy/blob/207dec0cee120a5898749d7b3f7b984341fff313/ivy/functional/frontends/scipy/fft/fft.py#L41
From the actual function definition, the argument should be `axes`
https://github.com/unifyai/ivy/blob/207dec0cee120a5898749d7b3f7b984341fff313/ivy/functional/ivy/experimental/layers.py#L2789-L2796


Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/scipy/fft/fft.py b/ivy/functional/frontends/scipy/fft/fft.py
--- a/ivy/functional/frontends/scipy/fft/fft.py
+++ b/ivy/functional/frontends/scipy/fft/fft.py
@@ -38,7 +38,7 @@ def ifft(x, n=None, axis=-1, norm=None, overwrite_x=False):
 def ifftn(
     x, s=None, axes=None, norm=None, overwrite_x=False, workers=None, *, plan=None
 ):
-    return ivy.ifftn(x, s=s, dim=axes, norm=norm)
+    return ivy.ifftn(x, s=s, axes=axes, norm=norm)
 
 
 @to_ivy_arrays_and_back


Test patch:
Repository: ivy-llc/ivy

Issue:
Unexpected keyword argument `pad_mode` in function call `ivy.avg_pool2d()`
In the following function call, the key-word argument `pad_mode` is used,
https://github.com/unifyai/ivy/blob/207dec0cee120a5898749d7b3f7b984341fff313/ivy/functional/frontends/mindspore/ops/function/nn_func.py#L146-L155
From the actual definition of the `ivy.avg_pool2d()`, the key-word argument should be `ceil_mode`
https://github.com/unifyai/ivy/blob/207dec0cee120a5898749d7b3f7b984341fff313/ivy/functional/ivy/experimental/layers.py#L375-L387


Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/mindspore/ops/function/nn_func.py b/ivy/functional/frontends/mindspore/ops/function/nn_func.py
--- a/ivy/functional/frontends/mindspore/ops/function/nn_func.py
+++ b/ivy/functional/frontends/mindspore/ops/function/nn_func.py
@@ -111,12 +111,12 @@ def adaptive_avg_pool2d(input, output_size):
 @to_ivy_arrays_and_back
 def avg_pool2d(
     input,
-    kernel_size,
-    stride=None,
+    kernel_size=1,
+    stride=1,
     padding=0,
-    pad_mode=False,
+    ceil_mode=False,
     count_include_pad=True,
-    divisor_override=None,
+    divisor_override=0,
 ):
     # Figure out input dims N
     input_rank = input.ndim
@@ -149,7 +149,7 @@ def avg_pool2d(
         stride,
         padding_str,
         data_format=data_format,
-        pad_mode=pad_mode,
+        ceil_mode=ceil_mode,
         count_include_pad=count_include_pad,
         divisor_override=divisor_override,
     )


Test patch:
Repository: ivy-llc/ivy

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



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
@@ -179,7 +179,7 @@ def ceil(x: paddle.Tensor, /, *, out: Optional[paddle.Tensor] = None) -> paddle.
 
 
 @with_supported_dtypes(
-    {"2.5.1 and below": ("float16", "float32", "float64", "complex")},
+    {"2.5.1 and below": ("float32", "float64", "complex")},
     backend_version,
 )
 def floor(x: paddle.Tensor, /, *, out: Optional[paddle.Tensor] = None) -> paddle.Tensor:


Test patch: