added the tanh_ to paddle tensor frontend

ivy/functional/backends/paddle/statistical.py

@@ -9,6 +9,7 @@
 from ivy.func_wrapper import (
     with_supported_dtypes,
     with_unsupported_dtypes,
+    with_supported_device_and_dtypes,
 )
 import ivy.functional.backends.paddle as paddle_backend
 from ivy.utils.einsum_parser import legalise_einsum_expr

ivy/functional/frontends/paddle/tensor/tensor.py

@@ -744,6 +744,11 @@ def remainder(self, y, name=None):
     def is_floating_point(self):
         return paddle_frontend.is_floating_point(self)
 
+    @with_supported_dtypes({"2.5.1 and below": ("float32", "float64")}, "paddle")
+    def tanh_(self, name=None):
+        y = self.tanh(self)
+        return ivy.inplace_update(self, y)
+
     @with_supported_dtypes({"2.5.1 and below": ("float32", "float64")}, "paddle")
     def reciprocal_(self, name=None):
         y = self.reciprocal(self)

ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_tensor.py

@@ -3990,6 +3990,41 @@ def test_paddle_tensor_tanh(
     )
 
 
+# tanh_
+@handle_frontend_method(
+    class_tree=CLASS_TREE,
+    init_tree="paddle.to_tensor",
+    method_name="tanh_",
+    dtype_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("valid"),
+    ),
+)
+def test_paddle_tensor_tanh_(
+    dtype_and_x,
+    frontend_method_data,
+    init_flags,
+    method_flags,
+    frontend,
+    on_device,
+    backend_fw,
+):
+    input_dtype, x = dtype_and_x
+    helpers.test_frontend_method(
+        init_input_dtypes=input_dtype,
+        backend_to_test=backend_fw,
+        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,
+    )
+
+
 # topk
 @handle_frontend_method(
     class_tree=CLASS_TREE,