From a5ed8164a2be69da3256d7dab585a430b8b94622 Mon Sep 17 00:00:00 2001
From: Deeptendu Santra <55111154+Dsantra92@users.noreply.github.com>
Date: Wed, 22 Feb 2023 18:11:43 +0530
Subject: [PATCH] Remove unsupported_dtypes (#10745)

---
 .../backends/tensorflow/linear_algebra.py     | 14 ----
 .../torch/experimental/elementwise.py         |  5 +-
 ivy/functional/frontends/tensorflow/nn.py     | 65 ++++++++++---------
 3 files changed, 40 insertions(+), 44 deletions(-)

diff --git a/ivy/functional/backends/tensorflow/linear_algebra.py b/ivy/functional/backends/tensorflow/linear_algebra.py
index e82b4c74ef2c0..68fd7e39e61af 100644
--- a/ivy/functional/backends/tensorflow/linear_algebra.py
+++ b/ivy/functional/backends/tensorflow/linear_algebra.py
@@ -796,17 +796,3 @@ def vector_to_skew_symmetric_matrix(
     # BS x 3 x 3
     ret = tf.concat((row1, row2, row3), -2)
     return ret
-
-
-vector_to_skew_symmetric_matrix.unsupported_dtypes = (
-    "int8",
-    "int16",
-    "int32",
-    "int64",
-    "uint8",
-    "uint16",
-    "uint32",
-    "uint64",
-    "float16",
-    "float64",
-)
diff --git a/ivy/functional/backends/torch/experimental/elementwise.py b/ivy/functional/backends/torch/experimental/elementwise.py
index b9d8b07510392..4fdfb292831c4 100644
--- a/ivy/functional/backends/torch/experimental/elementwise.py
+++ b/ivy/functional/backends/torch/experimental/elementwise.py
@@ -27,6 +27,10 @@ def lcm(
 lcm.support_native_out = True
 
 
+@with_unsupported_dtypes(
+    {"2.9.1 and below": ("bfloat16",)},
+    backend_version,
+)
 def fmod(
     x1: torch.Tensor,
     x2: torch.Tensor,
@@ -39,7 +43,6 @@ def fmod(
 
 
 fmod.support_native_out = True
-fmod.unsupported_dtypes = ("bfloat16",)
 
 
 def fmax(
diff --git a/ivy/functional/frontends/tensorflow/nn.py b/ivy/functional/frontends/tensorflow/nn.py
index c20294b08b0aa..d06e5c8dc5bd1 100644
--- a/ivy/functional/frontends/tensorflow/nn.py
+++ b/ivy/functional/frontends/tensorflow/nn.py
@@ -138,6 +138,7 @@ def conv3d_transpose(
     )
 
 
+@with_unsupported_dtypes({"2.9.1 and below": ("bfloat16",)}, "tensorflow")
 @to_ivy_arrays_and_back
 def depthwise_conv2d(
     input,
@@ -164,9 +165,6 @@ def depthwise_conv2d(
     )
 
 
-depthwise_conv2d.unsupported_dtypes = ("bfloat16",)
-
-
 @to_ivy_arrays_and_back
 def batch_normalization(x, mean, variance, offset, scale, variance_epsilon, name=None):
     inv = 1.0 / ivy.sqrt(variance + variance_epsilon)
@@ -183,22 +181,37 @@ def dropout(x, rate, noise_shape=None, seed=None, name=None):
     return ivy.dropout(x, rate, noise_shape=noise_shape, seed=seed)
 
 
+@with_unsupported_dtypes(
+    {
+        "2.9.1": (
+            "int8",
+            "int16",
+            "int32",
+            "int64",
+            "bool",
+            "bfloat16",
+        )
+    },
+    "tensorflow",
+)
 @to_ivy_arrays_and_back
 def silu(features, beta: float = 1.0):
     beta = ivy.astype(ivy.array(beta), ivy.dtype(features))
     return ivy.multiply(features, ivy.sigmoid(ivy.multiply(beta, features)))
 
 
-silu.unsupported_dtypes = (
-    "int8",
-    "int16",
-    "int32",
-    "int64",
-    "bool",
-    "bfloat16",
+@with_unsupported_dtypes(
+    {
+        "2.9.1": (
+            "int8",
+            "int16",
+            "int32",
+            "int64",
+            "bool",
+        )
+    },
+    "tensorflow",
 )
-
-
 @to_ivy_arrays_and_back
 def sigmoid_cross_entropy_with_logits(labels=None, logits=None, name=None):
     ivy.utils.assertions.check_shape(labels, logits)
@@ -210,15 +223,18 @@ def sigmoid_cross_entropy_with_logits(labels=None, logits=None, name=None):
     return ivy.add(ret_val, ivy.log1p(ivy.exp(neg_abs_logits)))
 
 
-sigmoid_cross_entropy_with_logits.unsupported_dtypes = (
-    "int8",
-    "int16",
-    "int32",
-    "int64",
-    "bool",
+@with_unsupported_dtypes(
+    {
+        "2.9.1": (
+            "int8",
+            "int16",
+            "int32",
+            "int64",
+            "bool",
+        )
+    },
+    "tensorflow",
 )
-
-
 @to_ivy_arrays_and_back
 def weighted_cross_entropy_with_logits(
     labels=None, logits=None, pos_weight=1.0, name=None
@@ -239,15 +255,6 @@ def weighted_cross_entropy_with_logits(
     return ivy.add(first_term, second_term)
 
 
-weighted_cross_entropy_with_logits.unsupported_dtypes = (
-    "int8",
-    "int16",
-    "int32",
-    "int64",
-    "bool",
-)
-
-
 @with_supported_dtypes(
     {"2.9.0 and below": ("float32", "float16", "bfloat16")}, "tensorflow"
 )