feat: add interpolate function and test

ivy/functional/backends/paddle/experimental/layers.py

@@ -1,6 +1,7 @@
 # global
 from typing import Optional, Union, Tuple, List, Literal, Sequence, Callable
 import paddle
+
 from ivy.functional.ivy.layers import (
     _handle_padding,
     _depth_max_pooling_helper,
@@ -436,10 +437,18 @@ def interpolate(
     scale_factor: Optional[Union[Sequence[int], int]] = None,
     recompute_scale_factor: Optional[bool] = None,
     align_corners: Optional[bool] = None,
-    antialias: Optional[bool] = False,
+    data_format: str = "NCHW",
     out: Optional[paddle.Tensor] = None,
 ):
-    raise IvyNotImplementedException()
+    if recompute_scale_factor is True:
+        align_mode = 1
+    elif recompute_scale_factor is False:
+        align_mode = 0
+    else:
+        align_mode = None
+    return paddle.nn.functional.interpolate(
+        x, size, scale_factor, mode, align_corners, align_mode, data_format
+    )
 
 
 def adaptive_max_pool2d(

ivy_tests/test_ivy/test_functional/test_experimental/test_nn/test_layers.py

@@ -1,4 +1,5 @@
 # global
+
 import numpy as np
 import torch
 from hypothesis import strategies as st, assume
@@ -1085,6 +1086,39 @@ def test_interpolate(
     )
 
 
+@handle_test(
+    fn_tree="functional.ivy.backends.experimental.paddle.interpolate",
+    dtype_x_mode=_interp_args(),
+    test_gradients=st.just(False),
+    number_positional_args=st.just(2),
+)
+def test_interpolate_paddle(dtype_x_mode, test_flags, backend_fw, fn_name, on_device):
+    (
+        input_dtype,
+        x,
+        mode,
+        size,
+        align_corners,
+        scale_factor,
+        recompute_scale_factor,
+    ) = dtype_x_mode
+    helpers.test_function(
+        input_dtypes=input_dtype,
+        test_flags=test_flags,
+        backend_to_test=backend_fw,
+        fn_name=fn_name,
+        on_device=on_device,
+        rtol_=1e-01,
+        atol_=1e-01,
+        x=x[0],
+        size=size,
+        mode=mode,
+        align_corners=align_corners,
+        scale_factor=scale_factor,
+        recompute_scale_factor=recompute_scale_factor,
+    )
+
+
 @handle_test(
     fn_tree="functional.ivy.experimental.max_pool1d",
     x_k_s_p=helpers.arrays_for_pooling(