Fix TFDebertaV2ConvLayer in TFDebertaV2Model (#16031)

* fix
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

src/transformers/models/deberta_v2/modeling_tf_deberta_v2.py

@@ -313,7 +313,7 @@ class TFDebertaV2ConvLayer(tf.keras.layers.Layer):
         rmask = tf.cast(1 - input_mask, tf.bool)
         out = tf.where(tf.broadcast_to(tf.expand_dims(rmask, -1), shape_list(out)), 0.0, out)
         out = self.dropout(out, training=training)
-        hidden_states = self.conv_act(out)
+        out = self.conv_act(out)
 
         layer_norm_input = residual_states + out
         output = self.LayerNorm(layer_norm_input)
@@ -323,10 +323,10 @@ class TFDebertaV2ConvLayer(tf.keras.layers.Layer):
         else:
             if len(shape_list(input_mask)) != len(shape_list(layer_norm_input)):
                 if len(shape_list(input_mask)) == 4:
-                    mask = tf.squeeze(tf.squeeze(input_mask, axis=1), axis=1)
-                mask = tf.cast(tf.expand_dims(input_mask, axis=2), tf.float32)
+                    input_mask = tf.squeeze(tf.squeeze(input_mask, axis=1), axis=1)
+                input_mask = tf.cast(tf.expand_dims(input_mask, axis=2), tf.float32)
 
-            output_states = output * mask
+            output_states = output * input_mask
 
         return output_states