lintfixbot: Auto-commit fixed lint errors in codebase

vedpatwardhan · Feb 17, 2023 · e44891b · e44891b
1 parent 9d1e37d
commit e44891b
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Showing 4 changed files with 23 additions and 11 deletions.
diff --git a/ivy/array/array.py b/ivy/array/array.py
@@ -262,7 +262,10 @@ def __ivy_array_function__(self, func, types, args, kwargs):
                 and (t.__ivy_array_function__ is not ivy.Array.__ivy_array_function__)
                 or (
                     hasattr(ivy.NativeArray, "__ivy_array_function__")
-                    and (t.__ivy_array_function__ is not ivy.NativeArray.__ivy_array_function__)
+                    and (
+                        t.__ivy_array_function__
+                        is not ivy.NativeArray.__ivy_array_function__
+                    )
                 )
             ):
                 return NotImplemented

diff --git a/ivy/functional/backends/torch/experimental/statistical.py b/ivy/functional/backends/torch/experimental/statistical.py
@@ -97,7 +97,9 @@ def quantile(
 
         temp = a.reshape((-1,) + tuple(desired_shape))
 
-        return torch.quantile(temp, q, dim=0, keepdim=keepdims, interpolation=interpolation)
+        return torch.quantile(
+            temp, q, dim=0, keepdim=keepdims, interpolation=interpolation
+        )
 
     return torch.quantile(a, q, dim=axis, keepdim=keepdims, interpolation=interpolation)
 

diff --git a/ivy/functional/frontends/torch/reduction_ops.py b/ivy/functional/frontends/torch/reduction_ops.py
@@ -163,13 +163,17 @@ def aminmax(input, *, dim=None, keepdim=False, out=None):
     "tensorflow": ("float16", "bfloat16"),
 }
 
+
 @to_ivy_arrays_and_back
-def quantile(input, q, dim=None, keepdim=False, *, interpolation='linear', out=None):
-    return ivy.quantile(input, q, axis=dim, keepdims=keepdim, interpolation=interpolation, out=out)
+def quantile(input, q, dim=None, keepdim=False, *, interpolation="linear", out=None):
+    return ivy.quantile(
+        input, q, axis=dim, keepdims=keepdim, interpolation=interpolation, out=out
+    )
+
 
 quantile.unsupported_dtypes = {
     "torch": ("float16", "bfloat16"),
     "numpy": ("float16", "bfloat16"),
     "jax": ("float16", "bfloat16"),
     "tensorflow": ("float16", "bfloat16"),
-}
+}
diff --git a/ivy_tests/test_ivy/test_frontends/test_torch/test_reduction_ops.py b/ivy_tests/test_ivy/test_frontends/test_torch/test_reduction_ops.py
@@ -8,7 +8,10 @@
 from ivy_tests.test_ivy.test_functional.test_core.test_statistical import (
     statistical_dtype_values,
 )
-from ivy_tests.test_ivy.test_functional.test_experimental.test_core.test_statistical import statistical_dtype_values as statistical_dtype_values_experimental
+from ivy_tests.test_ivy.test_functional.test_experimental.test_core.test_statistical import (
+    statistical_dtype_values as statistical_dtype_values_experimental,
+)
+
 
 @handle_frontend_test(
     fn_tree="torch.dist",
@@ -667,12 +670,12 @@ def test_torch_aminmax(
         keepdim=keepdims,
     )
 
+
 @handle_frontend_test(
     fn_tree="torch.quantile",
     dtype_and_x=statistical_dtype_values_experimental(function="quantile"),
-    keepdims=st.booleans()
+    keepdims=st.booleans(),
 )
-
 def test_torch_quantile(
     *,
     dtype_and_x,
@@ -684,7 +687,7 @@ def test_torch_quantile(
 ):
     input_dtype, x, axis, interpolation, q = dtype_and_x
     if type(axis) is tuple:
-        axis=axis[0]
+        axis = axis[0]
     helpers.test_frontend_function(
         input_dtypes=input_dtype,
         frontend=frontend,
@@ -695,5 +698,5 @@ def test_torch_quantile(
         q=q,
         dim=axis,
         keepdim=keepdims,
-        interpolation=interpolation[0]
-    )
+        interpolation=interpolation[0],
+    )