Update cache files [skip ci]

ivy/compiler/_cache/Translated_Outputs/Translated_BatchNorm2d_output/run_0/Translated__BatchNorm.py

@@ -47,11 +47,9 @@ def forward(self, input):
         """
         return torch.nn.functional.batch_norm(
             input,
-            (
-                self.running_mean
-                if not self.training or self.track_running_stats
-                else None
-            ),
+            self.running_mean
+            if not self.training or self.track_running_stats
+            else None,
             self.running_var if not self.training or self.track_running_stats else None,
             self.weight,
             self.bias,

ivy/compiler/_cache/Translated_Outputs/Translated_LayerNorm_output/run_0/Translated_LayerNorm.py

@@ -1,8 +1,8 @@
 import ivy.functional.frontends.torch as torch
 import ivy.functional.frontends.torch.nn as nn
 
-import numbers
 import typing
+import numbers
 
 from .helpers import Translated_ones_
 from .helpers import Translated_zeros_

ivy/compiler/_cache/Translated_Outputs/Translated_ModuleList_output/run_0/Translated_ModuleList.py

@@ -1,10 +1,10 @@
 import ivy.functional.frontends.torch.nn as nn
 
-import typing
 import operator
-from itertools import chain
-from collections import OrderedDict
+import typing
 from collections import abc as container_abcs
+from collections import OrderedDict
+from itertools import chain
 
 from .helpers import Translated__addindent
 

ivy/compiler/_cache/Translated_Outputs/Translated_Sequential_output/run_0/Translated_Sequential.py

@@ -1,11 +1,11 @@
 import ivy.functional.frontends.torch as torch
 import ivy.functional.frontends.torch.nn as nn
 
-import operator
 import typing
+import operator
 from collections import OrderedDict
-from typing import overload
 from itertools import islice
+from typing import overload
 
 
 class Translated_Sequential(nn.Module):

ivy/compiler/_cache/Translated_Outputs/ivy_BatchNorm2d_output/run_0/ivy__BatchNorm.py

@@ -47,11 +47,9 @@ def forward(self, input):
         """
         normalized, self.running_mean, self.running_var = ivy_batch_norm_frnt(
             input,
-            (
-                self.running_mean
-                if not self.training or self.track_running_stats
-                else None
-            ),
+            self.running_mean
+            if not self.training or self.track_running_stats
+            else None,
             self.running_var if not self.training or self.track_running_stats else None,
             self.weight,
             self.bias,

ivy/compiler/_cache/Translated_Outputs/ivy_LayerNorm_output/run_0/ivy_LayerNorm.py

@@ -2,8 +2,8 @@
 from collections import OrderedDict
 import threading
 
-import numbers
 import typing
+import numbers
 
 from .ivy__helpers import ivy_add_frnt_
 from .ivy__helpers import ivy_empty_frnt

ivy/compiler/_cache/Translated_Outputs/ivy_ModuleList_output/run_0/ivy_ModuleList.py

@@ -2,10 +2,10 @@
 from collections import OrderedDict
 import threading
 
-import typing
 import operator
-from itertools import chain
+import typing
 from collections import abc as container_abcs
+from itertools import chain
 
 from .ivy__helpers import ivy__addindent
 from .ivy__helpers import ivy_add_frnt_

ivy/compiler/_cache/Translated_Outputs/ivy_Sequential_output/run_0/ivy_Sequential.py

@@ -2,10 +2,10 @@
 from collections import OrderedDict
 import threading
 
-import operator
 import typing
-from typing import overload
+import operator
 from itertools import islice
+from typing import overload
 
 from .ivy__helpers import ivy_add_frnt_
 from .ivy__helpers import ivy_split_frnt_

ivy/compiler/_cache/Translated_Outputs/tensorflow_AdaptiveAvgPool2d_output/run_0/tensorflow_NestedSequence_bknd.py

@@ -1,5 +1,5 @@
-from typing import TypeVar
 from typing import Protocol
+from typing import TypeVar
 
 _T_co = TypeVar("_T_co", covariant=True)
 

ivy/compiler/_cache/Translated_Outputs/tensorflow_AdaptiveAvgPool2d_output/run_0/tensorflow__helpers.py

@@ -637,7 +637,9 @@ def tensorflow_default_bknd(
     return (
         x
         if tensorflow_exists_bknd(x)
-        else default_val() if default_callable else default_val
+        else default_val()
+        if default_callable
+        else default_val
     )
 
 
@@ -1027,27 +1029,21 @@ def tensorflow_nested_map_bknd(
         to_ignore = to_ignore + (class_instance,)
     tuple_check_fn = tensorflow_default_bknd(
         _tuple_check_fn,
-        (
-            (lambda x_, t_: isinstance(x_, t_))
-            if include_derived["tuple"]
-            else lambda x_, t_: type(x_) is t_
-        ),
+        (lambda x_, t_: isinstance(x_, t_))
+        if include_derived["tuple"]
+        else lambda x_, t_: type(x_) is t_,
     )
     list_check_fn = tensorflow_default_bknd(
         _list_check_fn,
-        (
-            (lambda x_, t_: isinstance(x_, t_))
-            if include_derived["list"]
-            else lambda x_, t_: type(x_) is t_
-        ),
+        (lambda x_, t_: isinstance(x_, t_))
+        if include_derived["list"]
+        else lambda x_, t_: type(x_) is t_,
     )
     dict_check_fn = tensorflow_default_bknd(
         _dict_check_fn,
-        (
-            (lambda x_, t_: isinstance(x_, t_))
-            if include_derived["dict"]
-            else lambda x_, t_: type(x_) is t_
-        ),
+        (lambda x_, t_: isinstance(x_, t_))
+        if include_derived["dict"]
+        else lambda x_, t_: type(x_) is t_,
     )
     if tuple_check_fn(x, tuple) and not isinstance(x, to_ignore):
         ret_list = [
@@ -1475,7 +1471,9 @@ def tensorflow_where(
         dtype = (
             x1.dtype
             if hasattr(x1, "dtype")
-            else x2.dtype if hasattr(x2, "dtype") else tensorflow_default_dtype_bknd()
+            else x2.dtype
+            if hasattr(x2, "dtype")
+            else tensorflow_default_dtype_bknd()
         )
         if not tensorflow_is_array_bknd(x1):
             x1 = tensorflow_asarray(x1, dtype=dtype)
@@ -1887,7 +1885,9 @@ def tensorflow__parse_query_bknd(query, x_shape, scatter=False):
             (
                 tensorflow_reshape_bknd_(arr, (-1,))
                 if len(arr.shape) > 1
-                else tensorflow_expand_dims(arr) if not len(arr.shape) else arr
+                else tensorflow_expand_dims(arr)
+                if not len(arr.shape)
+                else arr
             )
             for arr in array_queries
         ]
@@ -2082,11 +2082,9 @@ def is_native(x):
 
             if tensorflow_nested_argwhere_bknd(
                 input,
-                lambda x: (
-                    tensorflow_dtype(x) == "uint64"
-                    if is_native(x)
-                    else x > 9223372036854775807 and x != math.inf
-                ),
+                lambda x: tensorflow_dtype(x) == "uint64"
+                if is_native(x)
+                else x > 9223372036854775807 and x != math.inf,
                 stop_after_n_found=1,
             ):
                 ret = tf.uint64
@@ -2320,7 +2318,9 @@ def tensorflow_multiply(
         dtype = (
             x1.dtype
             if hasattr(x1, "dtype")
-            else x2.dtype if hasattr(x2, "dtype") else tensorflow_default_dtype_bknd()
+            else x2.dtype
+            if hasattr(x2, "dtype")
+            else tensorflow_default_dtype_bknd()
         )
         if not tensorflow_is_array_bknd(x1):
             x1 = tensorflow_asarray(x1, dtype=dtype)
@@ -2480,11 +2480,9 @@ def tensorflow_scatter_nd(
         dtype = tensorflow_promote_types_bknd(out.dtype, updates_dtype)
     updates = tensorflow.cast(
         updates,
-        (
-            tensorflow_as_native_dtype(dtype)
-            if tensorflow_exists_bknd(out)
-            else updates_dtype
-        ),
+        tensorflow_as_native_dtype(dtype)
+        if tensorflow_exists_bknd(out)
+        else updates_dtype,
     )
     expected_shape = (
         list(tensorflow.shape(indices)[:-1])
@@ -2848,21 +2846,17 @@ def tensorflow_default_int_dtype_bknd(
         elif isinstance(input, (list, tuple, dict)):
             if tensorflow_nested_argwhere_bknd(
                 input,
-                lambda x: (
-                    tensorflow_dtype(x) == "uint64"
-                    if tensorflow_is_array_bknd(x)
-                    else x > 9223372036854775807 and x != math.inf
-                ),
+                lambda x: tensorflow_dtype(x) == "uint64"
+                if tensorflow_is_array_bknd(x)
+                else x > 9223372036854775807 and x != math.inf,
                 stop_after_n_found=1,
             ):
                 ret = tf.uint64
             elif tensorflow_nested_argwhere_bknd(
                 input,
-                lambda x: (
-                    tensorflow_dtype(x) == "int64"
-                    if tensorflow_is_array_bknd(x)
-                    else x > 2147483647 and x != math.inf
-                ),
+                lambda x: tensorflow_dtype(x) == "int64"
+                if tensorflow_is_array_bknd(x)
+                else x > 2147483647 and x != math.inf,
                 stop_after_n_found=1,
             ):
                 ret = tf.int64
@@ -3028,7 +3022,9 @@ def tensorflow_add(
         dtype = (
             x1.dtype
             if hasattr(x1, "dtype")
-            else x2.dtype if hasattr(x2, "dtype") else tensorflow_default_dtype_bknd()
+            else x2.dtype
+            if hasattr(x2, "dtype")
+            else tensorflow_default_dtype_bknd()
         )
         if not tensorflow_is_array_bknd(x1):
             x1 = tensorflow_asarray(x1, dtype=dtype)
@@ -3316,7 +3312,9 @@ def tensorflow_divide(
         dtype = (
             x1.dtype
             if hasattr(x1, "dtype")
-            else x2.dtype if hasattr(x2, "dtype") else tensorflow_default_dtype_bknd()
+            else x2.dtype
+            if hasattr(x2, "dtype")
+            else tensorflow_default_dtype_bknd()
         )
         if not tensorflow_is_array_bknd(x1):
             x1 = tensorflow_asarray(x1, dtype=dtype)
@@ -3376,7 +3374,9 @@ def tensorflow_minimum(
         dtype = (
             x1.dtype
             if hasattr(x1, "dtype")
-            else x2.dtype if hasattr(x2, "dtype") else tensorflow_default_dtype_bknd()
+            else x2.dtype
+            if hasattr(x2, "dtype")
+            else tensorflow_default_dtype_bknd()
         )
         if not tensorflow_is_array_bknd(x1):
             x1 = tensorflow_asarray(x1, dtype=dtype)
@@ -3453,7 +3453,9 @@ def tensorflow_greater_equal(
         dtype = (
             x1.dtype
             if hasattr(x1, "dtype")
-            else x2.dtype if hasattr(x2, "dtype") else tensorflow_default_dtype_bknd()
+            else x2.dtype
+            if hasattr(x2, "dtype")
+            else tensorflow_default_dtype_bknd()
         )
         if not tensorflow_is_array_bknd(x1):
             x1 = tensorflow_asarray(x1, dtype=dtype)