Feature/add frontend function paddle tensor nanmean

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

@@ -640,3 +640,11 @@ def std(self, axis=None, unbiased=True, keepdim=False, name=None):
     )
     def trunc(self, name=None):
         return paddle_frontend.Tensor(ivy.trunc(self._ivy_array))
+
+    @with_supported_dtypes(
+        {"2.4.2 and below": ("uint16", "float16", "float32", "float64")}, "paddle"
+    )
+    def nanmean(self, axis=None, keepdim=False, name=None):
+        return paddle_frontend.Tensor(
+            ivy.nanmean(self._ivy_array, axis=axis, keepdims=keepdim)
+        )

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

@@ -2947,3 +2947,38 @@ def test_paddle_tensor_trunc(
         frontend=frontend,
         on_device=on_device,
     )
+
+
+@handle_frontend_method(
+    class_tree=CLASS_TREE,
+    init_tree="paddle.to_tensor",
+    method_name="nanmean",
+    dtype_and_x=_statistical_dtype_values(function="nanmean"),
+    keepdim=st.booleans(),
+)
+def test_paddle_tensor_nanmean(
+    dtype_and_x,
+    keepdim,
+    frontend,
+    backend_fw,
+    frontend_method_data,
+    init_flags,
+    method_flags,
+    on_device,
+):
+    input_dtype, x, axis, correction = dtype_and_x
+    helpers.test_frontend_method(
+        init_input_dtypes=input_dtype,
+        init_all_as_kwargs_np={"data": x[0]},
+        method_input_dtypes=input_dtype,
+        method_all_as_kwargs_np={
+            "axis": axis,
+            "keepdim": keepdim,
+        },
+        frontend=frontend,
+        frontend_method_data=frontend_method_data,
+        init_flags=init_flags,
+        backend_to_test=backend_fw,
+        method_flags=method_flags,
+        on_device=on_device,
+    )