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

Issue:
floor_divide
will implement floor_divide paddle frontend function


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
@@ -365,3 +365,8 @@ def imag(self, name=None):
 
     def is_tensor(self):
         return paddle_frontend.is_tensor(self._ivy_array)
+
+    @with_supported_dtypes({"2.5.0 and below": ("int32", "int64")}, "paddle")
+    def floor_divide(self, y, name=None):
+        y_ivy = y._ivy_array if isinstance(y, Tensor) else _to_ivy_array(y)
+        return ivy.floor_divide(self._ivy_array, y_ivy)


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_paddle_tensor.py b/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_paddle_tensor.py
--- a/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_paddle_tensor.py
+++ b/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_paddle_tensor.py
@@ -1851,6 +1851,45 @@ def test_paddle_imag(
     )
 
 
+# floor_divide
+@handle_frontend_method(
+    class_tree=CLASS_TREE,
+    init_tree="paddle.to_tensor",
+    method_name="floor_divide",
+    dtypes_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("valid"),
+        num_arrays=2,
+        min_value=2,
+        shared_dtype=True,
+        large_abs_safety_factor=2,
+        small_abs_safety_factor=2,
+        safety_factor_scale="linear",
+    ),
+)
+def test_paddle_floor_divide(
+    dtypes_and_x,
+    frontend_method_data,
+    init_flags,
+    method_flags,
+    frontend,
+    on_device,
+):
+    input_dtype, x = dtypes_and_x
+    # Absolute tolerance is 1,
+    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={"y": x[1]},
+        frontend_method_data=frontend_method_data,
+        init_flags=init_flags,
+        method_flags=method_flags,
+        frontend=frontend,
+        on_device=on_device,
+        atol_=1,
+    )
+
+
 # is_tensor
 @handle_frontend_method(
     class_tree=CLASS_TREE,
Repository: ivy-llc/ivy

Issue:
 logical_xor
adding  logical_xor in paddle.tensor.tesnor.Tesnor


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
@@ -223,3 +223,20 @@ def any(self, axis=None, keepdim=False, name=None):
     @with_supported_dtypes({"2.5.0 and below": ("float16", "bfloat16")}, "paddle")
     def astype(self, dtype):
         return ivy.astype(self._ivy_array, dtype=dtype)
+
+    @with_supported_dtypes(
+        {
+            "2.5.0 and below": (
+                "bool",
+                "int8",
+                "int16",
+                "int32",
+                "int64",
+                "float32",
+                "float64",
+            )
+        },
+        "paddle",
+    )
+    def logical_xor(self, y, out=None, name=None):
+        return paddle_frontend.logical_xor(self, y, out=out)


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_paddle_tensor.py b/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_paddle_tensor.py
--- a/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_paddle_tensor.py
+++ b/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_paddle_tensor.py
@@ -1285,3 +1285,34 @@ def test_paddle_bitwise_xor(
         frontend=frontend,
         on_device=on_device,
     )
+
+
+# logical_xor
+@handle_frontend_method(
+    class_tree=CLASS_TREE,
+    init_tree="paddle.to_tensor",
+    method_name="logical_xor",
+    dtypes_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("valid"), num_arrays=2, shared_dtype=True
+    ),
+)
+def test_paddle_logical_xor(
+    dtypes_and_x,
+    frontend_method_data,
+    init_flags,
+    method_flags,
+    frontend,
+    on_device,
+):
+    input_dtype, x = dtypes_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={"y": x[1]},
+        frontend_method_data=frontend_method_data,
+        init_flags=init_flags,
+        method_flags=method_flags,
+        frontend=frontend,
+        on_device=on_device,
+    )
Repository: ivy-llc/ivy

Issue:
 split



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
@@ -38,3 +38,12 @@ def concat(x, axis, name=None):
 @to_ivy_arrays_and_back
 def tile(x, repeat_times, name=None):
     return ivy.tile(x, repeats=repeat_times)
+
+
+@with_unsupported_dtypes(
+    {"2.5.0 and below": ("int16", "complex64", "complex128")},
+    "paddle",
+)
+@to_ivy_arrays_and_back
+def split(x, num_or_sections, axis=0, name=None):
+    return ivy.split(x, num_or_size_splits=num_or_sections, axis=axis)


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
@@ -246,3 +246,49 @@ def test_paddle_tile(
         x=x[0],
         repeat_times=repeats,
     )
+
+
+# split
+@st.composite
+def _split_helper(draw):
+    dtypes, values, shape = draw(
+        helpers.dtype_and_values(
+            available_dtypes=helpers.get_dtypes("valid"),
+            min_num_dims=2,
+            max_num_dims=4,
+            min_dim_size=2,
+            max_dim_size=4,
+            ret_shape=True,
+        )
+    )
+    axis = draw(st.sampled_from(range(len(shape))))
+    num_eles = shape[axis]
+    splits = [i for i in range(1, num_eles + 1) if num_eles % i == 0]
+    num_splits = draw(st.sampled_from(splits))
+    return dtypes, values, num_splits, axis
+
+
+@handle_frontend_test(
+    fn_tree="paddle.split",
+    dt_x_num_splits_axis=_split_helper(),
+    test_with_out=st.just(False),
+)
+def test_paddle_split(
+    *,
+    dt_x_num_splits_axis,
+    on_device,
+    fn_tree,
+    frontend,
+    test_flags,
+):
+    input_dtypes, x, num_splits, axis = dt_x_num_splits_axis
+    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],
+        num_or_sections=num_splits,
+        axis=axis,
+    )
Repository: ivy-llc/ivy

Issue:
packbits



Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/jax/numpy/logic.py b/ivy/functional/frontends/jax/numpy/logic.py
--- a/ivy/functional/frontends/jax/numpy/logic.py
+++ b/ivy/functional/frontends/jax/numpy/logic.py
@@ -8,6 +8,14 @@
 )
 from ivy.utils.exceptions import IvyNotImplementedException
 
+def _packbits_nested_list_padding(arr, pad_length):
+    if arr.ndim > 1:
+        nested_list = []
+        for sub_arr in arr:
+            nested_list.append(_packbits_nested_list_padding(sub_arr, pad_length))
+        return nested_list
+    else:
+        return arr.zero_pad(pad_width=[[0, pad_length]])
 
 @to_ivy_arrays_and_back
 def allclose(a, b, rtol=1e-05, atol=1e-08, equal_nan=False):
@@ -218,3 +226,25 @@ def iscomplex(x: any):
 @to_ivy_arrays_and_back
 def iscomplexobj(x):
     return ivy.is_complex_dtype(ivy.dtype(x))
+
+
+@to_ivy_arrays_and_back
+def packbits(x, /, *, axis=None, bitorder="big"):
+    x = ivy.greater(x, ivy.zeros_like(x)).astype("uint8")
+    bits = ivy.arange(8, dtype="uint8")
+    if bitorder == "big":
+        bits = bits[::-1]
+    if axis is None:
+        x = ivy.flatten(x)
+        axis = 0
+    x = ivy.swapaxes(x, axis, -1)
+
+    remainder = x.shape[-1] % 8
+    if remainder:
+        x = _packbits_nested_list_padding(x, 8 - remainder)
+        x = ivy.array(x)
+
+    x = ivy.reshape(x, list(x.shape[:-1]) + [x.shape[-1] // 8, 8])
+    bits = ivy.expand_dims(bits, axis=tuple(range(x.ndim - 1)))
+    packed = (x << bits).sum(axis=-1).astype("uint8")
+    return ivy.swapaxes(packed, axis, -1)


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_jax/test_numpy/test_logic.py b/ivy_tests/test_ivy/test_frontends/test_jax/test_numpy/test_logic.py
--- a/ivy_tests/test_ivy/test_frontends/test_jax/test_numpy/test_logic.py
+++ b/ivy_tests/test_ivy/test_frontends/test_jax/test_numpy/test_logic.py
@@ -967,3 +967,39 @@ def test_jax_iscomplexobj(
         on_device=on_device,
         x=x[0],
     )
+
+
+# packbits
+@handle_frontend_test(
+    fn_tree="jax.numpy.packbits",
+    dtype_x_axis=helpers.dtype_values_axis(
+        available_dtypes=helpers.get_dtypes("integer"),
+        min_num_dims=1,
+        min_dim_size=1,
+        valid_axis=True,
+        max_axes_size=1,
+        force_int_axis=True,
+    ),
+    test_with_out=st.just(False),
+    bitorder=st.sampled_from(["big", "little"]),
+)
+def test_jax_numpy_packbits(
+    dtype_x_axis,
+    bitorder,
+    frontend,
+    on_device,
+    *,
+    fn_tree,
+    test_flags,
+):
+    input_dtype, x, axis = dtype_x_axis
+    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],
+        axis=axis,
+        bitorder=bitorder,
+    )
Repository: ivy-llc/ivy

Issue:
bitwise_not



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
@@ -261,6 +261,13 @@ def any(self, axis=None, keepdim=False, name=None):
     def astype(self, dtype):
         return ivy.astype(self._ivy_array, dtype=dtype)
 
+    @with_supported_dtypes(
+        {"2.5.0 and below": ("bool", "uint8", "int8", "int16", "int32", "int64")},
+        "paddle",
+    )
+    def bitwise_not(self, out=None, name=None):
+        return ivy.bitwise_invert(self, out=out)
+
     @with_supported_dtypes(
         {
             "2.5.0 and below": (


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_paddle_tensor.py b/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_paddle_tensor.py
--- a/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_paddle_tensor.py
+++ b/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_paddle_tensor.py
@@ -1514,6 +1514,39 @@ def test_paddle_greater_than(
     )
 
 
+#  bitwise_not
+@handle_frontend_method(
+    class_tree=CLASS_TREE,
+    init_tree="paddle.to_tensor",
+    method_name="bitwise_not",
+    dtype_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("valid"),
+    ),
+)
+def test_paddle_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,
+        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,
+    )
+
+ 
 # reciprocal
 @handle_frontend_method(
     class_tree=CLASS_TREE,
Repository: ivy-llc/ivy

Issue:
gcd



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
@@ -234,7 +234,14 @@ def reciprocal(x, name=None):
     return ivy.reciprocal(x)
 
 
+@with_supported_dtypes({"2.5.0 and below": ("int32", "int64")}, "paddle")
+@to_ivy_arrays_and_back
+def gcd(x, y, name=None):
+    return ivy.gcd(x, y)
+
+  
 @with_unsupported_dtypes({"2.5.0 and below": "bfloat16"}, "paddle")
 @to_ivy_arrays_and_back
 def fmin(x, y, name=None):
     return ivy.fmin(x, y)
+


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
@@ -974,13 +974,15 @@ def test_paddle_cumprod(
     )
 
 
+
 # gcd
 @handle_frontend_test(
     fn_tree="paddle.gcd",
     dtype_and_x=helpers.dtype_and_values(
-        available_dtypes=helpers.get_dtypes("int"),
+        available_dtypes=helpers.get_dtypes("valid"),
         num_arrays=2,
-        shared_dtype=True,
+        min_num_dims=1,
+        shared_dtype=True
     ),
 )
 def test_paddle_gcd(
@@ -995,8 +997,8 @@ def test_paddle_gcd(
     helpers.test_frontend_function(
         input_dtypes=input_dtype,
         frontend=frontend,
-        test_flags=test_flags,
         fn_tree=fn_tree,
+        test_flags=test_flags,
         on_device=on_device,
         x=x[0],
         y=x[1],
Repository: ivy-llc/ivy

Issue:
Fix tensor.test_torch_instance_round
|   |   |
|---|---|
|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
@@ -1166,7 +1166,7 @@ def mul_(self, other):
         self.ivy_array = self.to(self.dtype).ivy_array
         return self
 
-    @with_unsupported_dtypes({"2.0.1 and below": ("float16",)}, "torch")
+    @with_unsupported_dtypes({"2.0.1 and below": ("bfloat16", "float16")}, "torch")
     def round(self, *, decimals=0):
         return torch_frontend.round(self, decimals=decimals)
 


Test patch:
Repository: ivy-llc/ivy

Issue:
flip



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
@@ -43,6 +43,15 @@ def tile(x, repeat_times, name=None):
     return ivy.tile(x, repeats=repeat_times)
 
 
+@with_unsupported_dtypes(
+    {"2.5.0 and below": ("int8", "uint8", "int16", "float16")},
+    "paddle",
+)
+@to_ivy_arrays_and_back
+def flip(x, axis, name=None):
+    return ivy.flip(x, axis=axis)
+
+
 @with_unsupported_dtypes(
     {"2.5.0 and below": ("int16", "complex64", "complex128")},
     "paddle",


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_manipulation.py b/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_manipulation.py
--- a/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_manipulation.py
+++ b/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_manipulation.py
@@ -450,7 +450,7 @@ def test_paddle_broadcast_to(
         x=x[0],
         shape=shape,
     )
-
+    
 
 @st.composite
 def _gather_helper(draw):
@@ -496,3 +496,47 @@ def test_paddle_gather(
         param=param[0],
         indices=indices[0],
     )
+    
+    
+# flip
+@st.composite
+def _dtype_x_axis(draw, **kwargs):
+    dtype, x, shape = draw(helpers.dtype_and_values(**kwargs, ret_shape=True))
+    axis = draw(
+        st.lists(
+            helpers.ints(min_value=0, max_value=len(shape) - 1),
+            min_size=len(shape),
+            max_size=len(shape),
+            unique=True,
+        )
+    )
+    return dtype, x, axis
+
+
+@handle_frontend_test(
+    fn_tree="paddle.flip",
+    dtype_x_axis=_dtype_x_axis(
+        available_dtypes=helpers.get_dtypes("numeric"),
+        min_num_dims=1,
+        min_dim_size=1,
+    ),
+    test_with_out=st.just(False),
+)
+def test_paddle_flip(
+    *,
+    dtype_x_axis,
+    on_device,
+    fn_tree,
+    frontend,
+    test_flags,
+):
+    input_dtype, x, axis = dtype_x_axis
+    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],
+        axis=axis,
+    )
Repository: ivy-llc/ivy

Issue:
greater_than
Adding greater_than in paddle tensor.py   


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
@@ -257,6 +257,22 @@ def astype(self, dtype):
     def logical_xor(self, y, out=None, name=None):
         return paddle_frontend.logical_xor(self, y, out=out)
 
+    @with_unsupported_dtypes(
+        {
+            "2.5.0 and below": (
+                "bool",
+                "uint8",
+                "int8",
+                "int16",
+                "complex64",
+                "complex128",
+            )
+        },
+        "paddle",
+    )
+    def greater_than(self, y, name=None):
+        return paddle_frontend.greater_than(self, y)
+
     @with_supported_dtypes({"2.5.0 and below": ("float32", "float64")}, "paddle")
     def rsqrt(self, name=None):
         return ivy.reciprocal(ivy.sqrt(self._ivy_array))


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_paddle_tensor.py b/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_paddle_tensor.py
--- a/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_paddle_tensor.py
+++ b/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_paddle_tensor.py
@@ -1380,3 +1380,38 @@ def test_paddle_rsqrt(
         frontend=frontend,
         on_device=on_device,
     )
+
+
+# greater_than
+@handle_frontend_method(
+    class_tree=CLASS_TREE,
+    init_tree="paddle.to_tensor",
+    method_name="greater_than",
+    dtypes_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("valid"),
+        num_arrays=2,
+        shared_dtype=True,
+        safety_factor_scale="log",
+        small_abs_safety_factor=32,
+    ),
+)
+def test_paddle_greater_than(
+    dtypes_and_x,
+    frontend_method_data,
+    init_flags,
+    method_flags,
+    frontend,
+    on_device,
+):
+    input_dtype, x = dtypes_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={"y": x[1]},
+        frontend_method_data=frontend_method_data,
+        init_flags=init_flags,
+        method_flags=method_flags,
+        frontend=frontend,
+        on_device=on_device,
+    )
Repository: ivy-llc/ivy

Issue:
uniform_ for paddle frontend



Discussion:

Resolving patch:
diff --git a/ivy/functional/frontends/paddle/tensor/random.py b/ivy/functional/frontends/paddle/tensor/random.py
--- a/ivy/functional/frontends/paddle/tensor/random.py
+++ b/ivy/functional/frontends/paddle/tensor/random.py
@@ -35,3 +35,15 @@ def randn(shape, dtype=None, name=None):
             "Unsupported dtype for randn, only float32 and float64 are supported, "
         )
     return ivy.random_normal(shape=shape, dtype=dtype, seed=None)
+
+
+@with_supported_dtypes(
+    {"2.5.0 and below": ("float32", "float64")},
+    "paddle",
+)
+@to_ivy_arrays_and_back
+def uniform_(x, min=-1.0, max=1.0, seed=0, name=None):
+    x = ivy.array(x)
+    return ivy.random_uniform(
+        low=min, high=max, shape=x.shape, dtype=x.dtype, seed=seed
+    )


Test patch:
diff --git a/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_paddle_random.py b/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_paddle_random.py
--- a/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_paddle_random.py
+++ b/ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_paddle_random.py
@@ -124,3 +124,41 @@ def test_paddle_randn(
         shape=shape,
         dtype=dtype,
     )
+
+
+@handle_frontend_test(
+    fn_tree="paddle.tensor.random.uniform_",
+    min=helpers.floats(min_value=-1, max_value=0),
+    max=helpers.floats(min_value=0.1, max_value=1),
+    seed=st.integers(min_value=2, max_value=5),
+    dtype_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("float"),
+        min_value=0,
+        max_value=1000,
+        min_num_dims=1,
+        max_num_dims=1,
+        min_dim_size=2,
+        max_dim_size=2,
+    ),
+)
+def test_paddle_uniform_(
+    fn_tree,
+    min,
+    max,
+    seed,
+    dtype_and_x,
+    frontend,
+    test_flags,
+):
+    dtype, x = dtype_and_x
+    helpers.test_frontend_function(
+        input_dtypes=dtype,
+        frontend=frontend,
+        test_flags=test_flags,
+        fn_tree=fn_tree,
+        test_values=False,
+        x=x[0],
+        min=min,
+        max=max,
+        seed=seed,
+    )