feat: updated tests for max_pool2d

ivy_tests/test_ivy/test_frontends/test_paddle/test_nn/test_functional/test_pooling.py

@@ -278,36 +278,54 @@ def test_paddle_avg_pool2d(
 
 # max_pool2d
 @handle_frontend_test(
-    fn_tree="paddle.nn.functional.max_pool2d",
+    fn_tree="paddle.nn.functional.pooling.max_pool2d",
+    dtype_x_k_s=helpers.arrays_for_pooling(
+        min_dims=4, max_dims=4, min_side=2, max_side=4
+    ),
+    ceil_mode=st.sampled_from([True]),
     data_format=st.sampled_from(["NCHW", "NHWC"]),
-    x_k_s_p=helpers.arrays_for_pooling(min_dims=4, max_dims=4, min_side=1, max_side=4),
-    stride=st.tuples(st.integers(1, 2), st.integers(1, 2)),
-    test_with_out=st.just(False),
 )
 def test_paddle_max_pool2d(
-    *,
-    x_k_s_p,
-    stride,
+    dtype_x_k_s,
+    ceil_mode,
     data_format,
-    frontend,
+    *,
     test_flags,
-    fn_tree,
     backend_fw,
+    frontend,
+    fn_tree,
     on_device,
 ):
-    input_dtype, x, kernel_size, _, padding = x_k_s_p
-    data_format = data_format
+    input_dtype, x, kernel, stride, padding = dtype_x_k_s
+
+    if data_format == "NCHW":
+        x[0] = x[0].reshape(
+            (x[0].shape[0], x[0].shape[3], x[0].shape[1], x[0].shape[2])
+        )
+    if len(stride) == 1:
+        stride = (stride[0], stride[0])
+    if padding == "SAME":
+        padding = test_pooling_functions.calculate_same_padding(
+            kernel, stride, x[0].shape[2:]
+        )
+    else:
+        padding = (0, 0)
+
+    if padding == "VALID" and ceil_mode:
+        ceil_mode = False
+
     helpers.test_frontend_function(
         input_dtypes=input_dtype,
+        test_flags=test_flags,
         backend_to_test=backend_fw,
         frontend=frontend,
-        test_flags=test_flags,
         fn_tree=fn_tree,
         on_device=on_device,
         x=x[0],
-        kernel_size=kernel_size,
+        kernel_size=kernel,
         stride=stride,
         padding=padding,
+        ceil_mode=ceil_mode,
         data_format=data_format,
     )