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

Issue:
var
Adding var which is a Statistical Function to PaddlePaddle Frontend


Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/paddle/tensor/stat.py b/ivy/functional/frontends/paddle/tensor/stat.py
--- a/ivy/functional/frontends/paddle/tensor/stat.py
+++ b/ivy/functional/frontends/paddle/tensor/stat.py
@@ -47,6 +47,16 @@ def median(x, axis=None, keepdim=False, name=None):
     return ivy.median(x, axis=axis, keepdims=keepdim)
 
 
+@with_supported_dtypes({"2.5.1 and below": ("float32", "float64")}, "paddle")
+@to_ivy_arrays_and_back
+def var(x, axis=None, unbiased=True, keepdim=False, name=None):
+    if unbiased:
+        correction = 1
+    else:
+        correction = 0
+    return ivy.var(x, axis=axis, correction=correction, keepdims=keepdim)
+
+
 @with_supported_dtypes(
     {"2.5.0 and below": ("float16", "float32", "float64", "uint16")},
     "paddle",


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_stat.py b/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_stat.py
--- a/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_stat.py
+++ b/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_stat.py
@@ -133,6 +133,37 @@ def test_paddle_median(
     )
 
 
+# var
+@handle_frontend_test(
+    fn_tree="paddle.var",
+    dtype_and_x=_statistical_dtype_values(function="var"),
+    unbiased=st.booleans(),
+    keepdim=st.booleans(),
+)
+def test_paddle_var(
+    *,
+    unbiased,
+    dtype_and_x,
+    keepdim,
+    fn_tree,
+    frontend,
+    backend_fw,
+    test_flags,
+):
+    input_dtype, x, axis, _ = 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,
+        x=x[0],
+        axis=axis,
+        unbiased=unbiased,
+        keepdim=keepdim,
+    )
+
+
 @handle_frontend_test(
     fn_tree="paddle.nanmedian",
     dtype_x_and_axis=helpers.dtype_values_axis(
Repository: ivy-llc/ivy

Issue:
corrcoef



Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/torch/miscellaneous_ops.py b/ivy/functional/frontends/torch/miscellaneous_ops.py
--- a/ivy/functional/frontends/torch/miscellaneous_ops.py
+++ b/ivy/functional/frontends/torch/miscellaneous_ops.py
@@ -426,3 +426,13 @@ def view_as_real(input):
     re_part = ivy.real(input)
     im_part = ivy.imag(input)
     return ivy.stack((re_part, im_part), axis=-1)
+
+
+@to_ivy_arrays_and_back
+def corrcoef(input):
+    if len(ivy.shape(input)) > 2:
+        raise ivy.exceptions.IvyError(
+            "corrcoef(): expected input to have two or fewer dimensions but got an"
+            f" input with {ivy.shape(input)} dimansions"
+        )
+    return ivy.corrcoef(input, y=None, rowvar=True)


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_torch/test_miscellaneous_ops.py b/ivy_tests/test_ivy/test_frontends/test_torch/test_miscellaneous_ops.py
--- a/ivy_tests/test_ivy/test_frontends/test_torch/test_miscellaneous_ops.py
+++ b/ivy_tests/test_ivy/test_frontends/test_torch/test_miscellaneous_ops.py
@@ -1584,3 +1584,37 @@ def test_torch_view_as_real(
         on_device=on_device,
         input=np.asarray(x[0], dtype=input_dtype[0]),
     )
+
+
+# corrcoef
+@handle_frontend_test(
+    fn_tree="torch.corrcoef",
+    dtypes_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("float"),
+        num_arrays=1,
+        min_num_dims=2,
+        max_num_dims=2,
+        min_dim_size=2,
+        max_dim_size=2,
+        min_value=1,
+    ),
+    test_with_out=st.just(False),
+)
+def test_torch_corrcoef(
+    dtypes_and_x,
+    frontend,
+    fn_tree,
+    on_device,
+    test_flags,
+    backend_fw,
+):
+    input_dtypes, x = dtypes_and_x
+    helpers.test_frontend_function(
+        input_dtypes=["float64"],
+        frontend=frontend,
+        fn_tree=fn_tree,
+        test_flags=test_flags,
+        on_device=on_device,
+        backend_to_test=backend_fw,
+        input=x[0],
+    )
Repository: ivy-llc/ivy

Issue:
<zeros_initializer>



Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/tensorflow/general_functions.py b/ivy/functional/frontends/tensorflow/general_functions.py
--- a/ivy/functional/frontends/tensorflow/general_functions.py
+++ b/ivy/functional/frontends/tensorflow/general_functions.py
@@ -650,3 +650,11 @@ def truncatemod(x, y):
 @to_ivy_arrays_and_back
 def unravel_index(indices, dims, out=None, name=None):
     return ivy.unravel_index(indices, dims, out=out)
+
+
+@with_unsupported_dtypes({"2.13.0 and below": ("float16", "bfloat16")}, "tensorflow")
+@to_ivy_arrays_and_back
+def zeros_initializer(shape, dtype=None, name=None):
+    if dtype is None:
+        dtype = ivy.default_dtype()
+    return ivy.zeros(shape, dtype=dtype)
\ No newline at end of file


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_tensorflow/test_general_functions.py b/ivy_tests/test_ivy/test_frontends/test_tensorflow/test_general_functions.py
--- a/ivy_tests/test_ivy/test_frontends/test_tensorflow/test_general_functions.py
+++ b/ivy_tests/test_ivy/test_frontends/test_tensorflow/test_general_functions.py
@@ -2329,3 +2329,35 @@ def test_tensorflow_unravel_index(
         indices=indices[0],
         dims=dims[0],
     )
+
+@handle_frontend_test(
+    fn_tree="tensorflow.zeros_initializer",
+    shape=helpers.get_shape(
+        allow_none=False,
+        min_num_dims=1,
+        max_num_dims=5,
+        min_dim_size=1,
+        max_dim_size=10,
+    ),
+    dtype=helpers.get_dtypes("valid", full=False),
+    test_with_out=st.just(False),
+)
+def test_tensorflow_zeros_initializer(
+    shape,
+    dtype,
+    frontend,
+    backend_fw,
+    test_flags,
+    fn_tree,
+    on_device,
+):
+    helpers.test_frontend_function(
+        input_dtypes=dtype,
+        frontend=frontend,
+        backend_to_test=backend_fw,
+        test_flags=test_flags,
+        fn_tree=fn_tree,
+        on_device=on_device,
+        shape=shape,
+        dtype=dtype[0],
+    )
\ No newline at end of file
Repository: ivy-llc/ivy

Issue:
erfc



Discussion:

Resolving patch:
diff --git a/ivy/functional/backends/paddle/layers.py b/ivy/functional/backends/paddle/layers.py
--- a/ivy/functional/backends/paddle/layers.py
+++ b/ivy/functional/backends/paddle/layers.py
@@ -157,7 +157,7 @@ def conv1d(
 
 
 @with_unsupported_device_and_dtypes(
-    {"2.5.0 and below": {"cpu": ("float16", "bfloat16")}},
+    {"2.5.1 and below": {"cpu": ("float16", "bfloat16")}},
     backend_version,
 )
 def conv1d_transpose(
diff --git a/ivy/functional/frontends/jax/lax/operators.py b/ivy/functional/frontends/jax/lax/operators.py
--- a/ivy/functional/frontends/jax/lax/operators.py
+++ b/ivy/functional/frontends/jax/lax/operators.py
@@ -673,3 +673,20 @@ def cbrt(x):
 @to_ivy_arrays_and_back
 def tie_in(x, y):
     return y
+
+
+@with_supported_dtypes(
+    {
+        "0.4.14 and below": (
+            "float16",
+            "float32",
+            "float64",
+        )
+    },
+    "jax",
+)
+@to_ivy_arrays_and_back
+def erfc(x):
+    value = ivy.erf(x)
+    value = (1.0 - value) if value is not None else None
+    return value


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_jax/test_lax/test_operators.py b/ivy_tests/test_ivy/test_frontends/test_jax/test_lax/test_operators.py
--- a/ivy_tests/test_ivy/test_frontends/test_jax/test_lax/test_operators.py
+++ b/ivy_tests/test_ivy/test_frontends/test_jax/test_lax/test_operators.py
@@ -3021,3 +3021,32 @@ def test_jax_tie_in(
         x=x[0],
         y=x[1],
     )
+
+
+# erfc
+@handle_frontend_test(
+    fn_tree="jax.lax.erfc",
+    dtype_and_x=helpers.dtype_and_values(available_dtypes=helpers.get_dtypes("valid")),
+    test_with_out=st.just(False),
+)
+def test_jax_erfc(
+    *,
+    dtype_and_x,
+    on_device,
+    fn_tree,
+    frontend,
+    test_flags,
+    backend_fw,
+):
+    input_dtype, x = dtype_and_x
+    helpers.test_frontend_function(
+        input_dtypes=input_dtype,
+        backend_to_test=backend_fw,
+        frontend=frontend,
+        test_flags=test_flags,
+        fn_tree=fn_tree,
+        on_device=on_device,
+        rtol=1e-2,
+        atol=1e-2,
+        x=x[0],
+    )
Repository: ivy-llc/ivy

Issue:
tie_in



Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/jax/lax/operators.py b/ivy/functional/frontends/jax/lax/operators.py
--- a/ivy/functional/frontends/jax/lax/operators.py
+++ b/ivy/functional/frontends/jax/lax/operators.py
@@ -657,3 +657,8 @@ def is_finite(x):
 @to_ivy_arrays_and_back
 def cbrt(x):
     return ivy.pow(x, 1 / 3)
+
+
+@to_ivy_arrays_and_back
+def tie_in(x, y):
+    return y


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_jax/test_lax/test_operators.py b/ivy_tests/test_ivy/test_frontends/test_jax/test_lax/test_operators.py
--- a/ivy_tests/test_ivy/test_frontends/test_jax/test_lax/test_operators.py
+++ b/ivy_tests/test_ivy/test_frontends/test_jax/test_lax/test_operators.py
@@ -2987,3 +2987,35 @@ def test_jax_cbrt(
         on_device=on_device,
         x=x[0],
     )
+
+
+# tie_in
+@handle_frontend_test(
+    fn_tree="jax.lax.tie_in",
+    dtype_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("valid"),
+        num_arrays=2,
+        shared_dtype=True,
+    ),
+    test_with_out=st.just(False),
+)
+def test_jax_tie_in(
+    *,
+    dtype_and_x,
+    on_device,
+    fn_tree,
+    frontend,
+    test_flags,
+    backend_fw,
+):
+    input_dtype, x = dtype_and_x
+    helpers.test_frontend_function(
+        input_dtypes=input_dtype,
+        backend_to_test=backend_fw,
+        frontend=frontend,
+        test_flags=test_flags,
+        fn_tree=fn_tree,
+        on_device=on_device,
+        x=x[0],
+        y=x[1],
+    )
Repository: ivy-llc/ivy

Issue:
dropout 3d mindspore
- [dropout3d] #21020 


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
@@ -37,6 +37,15 @@ def selu(input_x):
 def softsign(x):
     return ivy.divide(x, ivy.add(1, ivy.abs(x)))
 
+@with_supported_dtypes(
+    {"2.0.0 and below": ("int8", "int16", "int32", "int64", "float16", "float32", "float64")},
+    "mindspore",
+)
+@to_ivy_arrays_and_back
+def dropout3d(input, p=0.5, training=True):
+    return ivy.dropout3d(input, p, training=training, data_format="NCDHW")
+
+
 
 @with_supported_dtypes(
     {


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_mindspore/test_ops/test_function/test_mindspore_nn_func.py b/ivy_tests/test_ivy/test_frontends/test_mindspore/test_ops/test_function/test_mindspore_nn_func.py
--- a/ivy_tests/test_ivy/test_frontends/test_mindspore/test_ops/test_function/test_mindspore_nn_func.py
+++ b/ivy_tests/test_ivy/test_frontends/test_mindspore/test_ops/test_function/test_mindspore_nn_func.py
@@ -78,6 +78,48 @@
 #         x=x[0],
 #     )
 
+# dropout3d
+# @handle_frontend_test(
+#     fn_tree="mindspore.ops.function.nn_func.dropout3d",
+#     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=5,
+#         shape=(
+#             st.integers(min_value=2, max_value=10),
+#             st.integers(min_value=12, max_value=64),
+#             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(),
+# )
+# def test_mindspore_dropout3d(
+#     *,
+#     d_type_and_x,
+#     p,
+#     training,
+#     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,
+#         input=x[0],
+#         p=p,
+#         training=training,
+#     )
+
 
 # pad
 # @handle_frontend_test(
Repository: ivy-llc/ivy

Issue:
flatten
- [flatten] #21022  


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
@@ -218,6 +218,11 @@ def avg_pool2d(
     )
 
 
+@to_ivy_arrays_and_back
+def flatten(input, order="C", *, start_dim=1, end_dim=-1):
+    return ivy.flatten(input, order=order, start_dim=start_dim, end_dim=end_dim)
+
+
 @with_supported_dtypes(
     {"2.0.0 and below": ("float16", "float32", "float64")},
     "mindspore",


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_mindspore/test_ops/test_function/test_mindspore_nn_func.py b/ivy_tests/test_ivy/test_frontends/test_mindspore/test_ops/test_function/test_mindspore_nn_func.py
--- a/ivy_tests/test_ivy/test_frontends/test_mindspore/test_ops/test_function/test_mindspore_nn_func.py
+++ b/ivy_tests/test_ivy/test_frontends/test_mindspore/test_ops/test_function/test_mindspore_nn_func.py
@@ -403,6 +403,7 @@
 #         divisor_override=None,
 #     )
 
+
 # FastGelu
 # @handle_frontend_test(
 #     fn_tree="mindspore.ops.function.nn_func.fast_gelu",
@@ -428,3 +429,44 @@
 #         on_device=on_device,
 #         input=x[0],
 #     )
+
+
+# # flatten
+# @handle_frontend_test(
+#     fn_tree="mindspore.ops.function.nn_func.flatten",
+#     dtype_input_axes=helpers.dtype_values_axis(
+#         available_dtypes=helpers.get_dtypes("valid"),
+#         valid_axis=True,
+#         min_num_dims=1,
+#         min_axes_size=2,
+#         max_axes_size=2,
+#     ),
+# )
+# def test_mindspore_flatten(
+#     *,
+#     dtype_input_axes,
+#     on_device,
+#     fn_tree,
+#     frontend,
+#     test_flags,
+#     backend_fw,
+# ):
+#     dtype, input, axes = dtype_input_axes
+#     if isinstance(axes, int):
+#         start_dim = axes
+#         end_dim = -1
+#     else:
+#         start_dim = axes[0]
+#         end_dim = axes[1]
+#     helpers.test_frontend_function(
+#         input_dtypes=dtype,
+#         backend_to_test=backend_fw,
+#         frontend=frontend,
+#         test_flags=test_flags,
+#         fn_tree=fn_tree,
+#         on_device=on_device,
+#         input=input[0],
+#         order='C',
+#         start_dim=start_dim,
+#         end_dim=end_dim,
+#     )
Repository: ivy-llc/ivy

Issue:
igamma



Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/torch/pointwise_ops.py b/ivy/functional/frontends/torch/pointwise_ops.py
--- a/ivy/functional/frontends/torch/pointwise_ops.py
+++ b/ivy/functional/frontends/torch/pointwise_ops.py
@@ -557,3 +557,8 @@ def nan_to_num(input, nan=0.0, posinf=None, neginf=None, *, out=None):
 @to_ivy_arrays_and_back
 def masked_fill(input, mask, value):
     return ivy.where(mask, value, input, out=input)
+
+
+@to_ivy_arrays_and_back
+def igamma(input, other, *, out=None):
+    return ivy.igamma(input, x=other, out=out)


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
@@ -10597,6 +10597,42 @@ def test_torch_instance_conj(
     )
 
 
+# igamma
+@handle_frontend_method(
+    class_tree=CLASS_TREE,
+    init_tree="torch.tensor",
+    method_name="igamma",
+    dtype_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("valid"),
+        num_arrays=2,
+        shared_dtype=True,
+        min_num_dims=1,
+        min_value=-100,
+        max_value=100,
+    ),
+)
+def test_torch_igamma(
+    dtype_and_x,
+    frontend,
+    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={"other": x[1]},
+        frontend=frontend,
+        frontend_method_data=frontend_method_data,
+        init_flags=init_flags,
+        method_flags=method_flags,
+        on_device=on_device,
+    )
+
+
 @st.composite
 def _get_clip_min_inputs(draw):
     shape = draw(
Repository: ivy-llc/ivy

Issue:
svd



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
@@ -1613,6 +1613,10 @@ def adjoint(self):
     def conj(self):
         return torch_frontend.conj(self)
 
+    @with_unsupported_dtypes({"2.0.1 and below": ("float16", "bfloat16")}, "torch")
+    def svd(self, some=True, compute_uv=True, *, out=None):
+        return torch_frontend.svd(self, some=some, compute_uv=compute_uv, out=out)
+
 
 class Size(tuple):
     def __new__(cls, iterable=()):


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
@@ -10597,6 +10597,78 @@ def test_torch_instance_conj(
     )
 
 
+@handle_frontend_method(
+    class_tree=CLASS_TREE,
+    init_tree="torch.tensor",
+    method_name="svd",
+    dtype_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("float"),
+        min_value=0,
+        max_value=10,
+        shape=helpers.ints(min_value=2, max_value=5).map(lambda x: tuple([x, x])),
+    ),
+    some=st.booleans(),
+    compute_uv=st.booleans(),
+)
+def test_torch_instance_svd(
+    dtype_and_x,
+    some,
+    compute_uv,
+    frontend,
+    backend_fw,
+    frontend_method_data,
+    init_flags,
+    method_flags,
+    on_device,
+):
+    input_dtype, x = dtype_and_x
+    x = np.asarray(x[0], dtype=input_dtype[0])
+
+    ret, frontend_ret = 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={
+            "some": some,
+            "compute_uv": compute_uv,
+        },
+        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,
+        test_values=False,
+    )
+    with helpers.update_backend(backend_fw) as ivy_backend:
+        ret = [ivy_backend.to_numpy(x) for x in ret]
+    frontend_ret = [np.asarray(x) for x in frontend_ret]
+
+    u, s, vh = ret
+    frontend_u, frontend_s, frontend_vh = frontend_ret
+
+    if compute_uv:
+        helpers.assert_all_close(
+            ret_np=frontend_u @ np.diag(frontend_s) @ frontend_vh.T,
+            ret_from_gt_np=u @ np.diag(s) @ vh,
+            rtol=1e-2,
+            atol=1e-2,
+            backend=backend_fw,
+            ground_truth_backend=frontend,
+        )
+    else:
+        helpers.assert_all_close(
+            ret_np=frontend_s,
+            ret_from_gt_np=s,
+            rtol=1e-2,
+            atol=1e-2,
+            backend=backend_fw,
+            ground_truth_backend=frontend,
+        )
+
+
 @st.composite
 def _get_clip_min_inputs(draw):
     shape = draw(
Repository: ivy-llc/ivy

Issue:
pad
added for support pad in midspore

- [pad] #21041




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
@@ -36,3 +36,8 @@ def selu(input_x):
 @to_ivy_arrays_and_back
 def softsign(x):
     return ivy.divide(x, ivy.add(1, ivy.abs(x)))
+
+@with_supported_dtypes({"2.0 and below": ("int8", "int16", "int32", "int64", "float16", "float32", "float64")}, "mindspore")
+@to_ivy_arrays_and_back
+def pad(input, pad_width, mode='constant', constant_values=0):
+    return ivy.pad(input, pad_width, mode=mode, constant_values=constant_values)
\ No newline at end of file


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_mindspore/test_ops/test_function/test_mindspore_nn_func.py b/ivy_tests/test_ivy/test_frontends/test_mindspore/test_ops/test_function/test_mindspore_nn_func.py
--- a/ivy_tests/test_ivy/test_frontends/test_mindspore/test_ops/test_function/test_mindspore_nn_func.py
+++ b/ivy_tests/test_ivy/test_frontends/test_mindspore/test_ops/test_function/test_mindspore_nn_func.py
@@ -77,3 +77,42 @@
 #         on_device=on_device,
 #         x=x[0],
 #     )
+
+
+# pad
+# @handle_frontend_test(
+#     fn_tree="pad",
+#     input=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,
+#     ),
+#     pad_width=st.lists(st.tuples(st.integers(min_value=0, max_value=5), st.integers(min_value=0, max_value=5))),
+#     mode=st.sampled_from(['constant', 'reflect', 'replicate', 'circular']),
+#     constant_values=st.floats(min_value=0.0, max_value=1.0),
+# )
+# def test_mindspore_pad(
+#     *,
+#     input,
+#     pad_width,
+#     mode,
+#     constant_values,
+#     on_device,
+#     fn_tree,
+#     frontend,
+#     test_flags,
+# ):
+#     helpers.test_frontend_function(
+#         input_dtypes=input[0],
+#         frontend=frontend,
+#         test_flags=test_flags,
+#         fn_tree=fn_tree,
+#         on_device=on_device,
+#         input=input[1],
+#         pad_width=pad_width,
+#         mode=mode,
+#         constant_values=constant_values,
+#     )