Repository: Project-MONAI/MONAI

I have access to a python code repository in the directory /testbed . You can explore and modify files using the available tools. Consider the following issue description:

<issue_description>
Update TileOnGrid backend
**Is your feature request related to a problem? Please describe.**
Update `TileOnGrid` to support input/output type of `torch.Tensor` and `numpy` while its backend is `numpy` only.
</issue_description>

Can you help me implement the necessary changes to the repository so that the requirements specified in the <issue_description> are met?
I've already taken care of all changes to any of the test files described in the <issue_description>. This means you DON'T have to modify the testing logic or any of the tests in any way!
Also the development Python environment is already set up for you (i.e., all dependencies already installed), so you don't need to install other packages.
Your task is to make the minimal changes to non-test files in the /testbed directory to ensure the <issue_description> is satisfied.

Follow these phases to resolve the issue:

Phase 1. READING: read the problem and reword it in clearer terms
   1.1 If there are code or config snippets. Express in words any best practices or conventions in them.
   1.2 Hightlight message errors, method names, variables, file names, stack traces, and technical details.
   1.3 Explain the problem in clear terms.
   1.4 Enumerate the steps to reproduce the problem.
   1.5 Hightlight any best practices to take into account when testing and fixing the issue

Phase 2. RUNNING: install and run the tests on the repository
   2.1 Activate the environment by running  
       ./opt/miniconda3/etc/profile.d/conda.sh ; conda activate testbed
   2.2 Follow the readme
   2.3 Install the environment and anything needed
   2.4 Iterate and figure out how to run the tests

Phase 3. EXPLORATION: find the files that are related to the problem and possible solutions
   3.1 Use `grep` to search for relevant methods, classes, keywords and error messages.
   3.2 Identify all files related to the problem statement.
   3.3 Propose the methods and files to fix the issue and explain why.
   3.4 From the possible file locations, select the most likely location to fix the issue.

Phase 4. TEST CREATION: before implementing any fix, create a script to reproduce and verify the issue.
   4.1 Look at existing test files in the repository to understand the test format/structure.
   4.2 Create a minimal reproduction script that reproduces the located issue.
   4.3 Run the reproduction script to confirm you are reproducing the issue.
   4.4 Adjust the reproduction script as necessary.

Phase 5. FIX ANALYSIS: state clearly the problem and how to fix it
   5.1 State clearly what the problem is.
   5.2 State clearly where the problem is located.
   5.3 State clearly how the test reproduces the issue.
   5.4 State clearly the best practices to take into account in the fix.
   5.5 State clearly how to fix the problem.

Phase 6. FIX IMPLEMENTATION: Edit the source code to implement your chosen solution.
   6.1 Make minimal, focused changes to fix the issue.

Phase 7. VERIFICATION: Test your implementation thoroughly.
   7.1 Run your reproduction script to verify the fix works.
   7.2 Add edge cases to your test script to ensure comprehensive coverage.
   7.3 Run existing tests related to the modified code to ensure you haven't broken anything.

8. FINAL REVIEW: Carefully re-read the problem description and compare your changes with the base commit 5adf30beb3581e951556fd8c4f027828bf367a70.
   8.1 Ensure you've fully addressed all requirements.
   8.2 Run any tests in the repository related to:
     8.2.1 The issue you are fixing
     8.2.2 The files you modified
     8.2.3 The functions you changed
   8.3 If any tests fail, revise your implementation until all tests pass

Be thorough in your exploration, testing, and reasoning. It's fine if your thinking process is lengthy - quality and completeness are more important than brevity.

Gold patch:
diff --git a/monai/apps/pathology/transforms/spatial/array.py b/monai/apps/pathology/transforms/spatial/array.py
--- a/monai/apps/pathology/transforms/spatial/array.py
+++ b/monai/apps/pathology/transforms/spatial/array.py
@@ -17,6 +17,7 @@
 
 from monai.config.type_definitions import NdarrayOrTensor
 from monai.transforms.transform import Randomizable, Transform
+from monai.utils import convert_data_type, convert_to_dst_type
 from monai.utils.enums import TransformBackends
 
 __all__ = ["SplitOnGrid", "TileOnGrid"]
@@ -129,6 +130,8 @@ class TileOnGrid(Randomizable, Transform):
 
     """
 
+    backend = [TransformBackends.NUMPY]
+
     def __init__(
         self,
         tile_count: Optional[int] = None,
@@ -185,37 +188,39 @@ def randomize(self, img_size: Sequence[int]) -> None:
         else:
             self.random_idxs = np.array((0,))
 
-    def __call__(self, image: np.ndarray) -> np.ndarray:
+    def __call__(self, image: NdarrayOrTensor) -> NdarrayOrTensor:
+        img_np: np.ndarray
+        img_np, *_ = convert_data_type(image, np.ndarray)  # type: ignore
 
         # add random offset
-        self.randomize(img_size=image.shape)
+        self.randomize(img_size=img_np.shape)
 
         if self.random_offset and (self.offset[0] > 0 or self.offset[1] > 0):
-            image = image[:, self.offset[0] :, self.offset[1] :]
+            img_np = img_np[:, self.offset[0] :, self.offset[1] :]
 
         # pad to full size, divisible by tile_size
         if self.pad_full:
-            c, h, w = image.shape
+            c, h, w = img_np.shape
             pad_h = (self.tile_size - h % self.tile_size) % self.tile_size
             pad_w = (self.tile_size - w % self.tile_size) % self.tile_size
-            image = np.pad(
-                image,
+            img_np = np.pad(
+                img_np,
                 [[0, 0], [pad_h // 2, pad_h - pad_h // 2], [pad_w // 2, pad_w - pad_w // 2]],
                 constant_values=self.background_val,
             )
 
         # extact tiles
-        x_step, y_step = self.step, self.step
-        x_size, y_size = self.tile_size, self.tile_size
-        c_len, x_len, y_len = image.shape
-        c_stride, x_stride, y_stride = image.strides
+        h_step, w_step = self.step, self.step
+        h_size, w_size = self.tile_size, self.tile_size
+        c_len, h_len, w_len = img_np.shape
+        c_stride, h_stride, w_stride = img_np.strides
         llw = as_strided(
-            image,
-            shape=((x_len - x_size) // x_step + 1, (y_len - y_size) // y_step + 1, c_len, x_size, y_size),
-            strides=(x_stride * x_step, y_stride * y_step, c_stride, x_stride, y_stride),
+            img_np,
+            shape=((h_len - h_size) // h_step + 1, (w_len - w_size) // w_step + 1, c_len, h_size, w_size),
+            strides=(h_stride * h_step, w_stride * w_step, c_stride, h_stride, w_stride),
             writeable=False,
         )
-        image = llw.reshape(-1, c_len, x_size, y_size)
+        img_np = llw.reshape(-1, c_len, h_size, w_size)
 
         # if keeping all patches
         if self.tile_count is None:
@@ -224,32 +229,34 @@ def __call__(self, image: np.ndarray) -> np.ndarray:
             thresh = 0.999 * 3 * self.background_val * self.tile_size * self.tile_size
             if self.filter_mode == "min":
                 # default, keep non-background tiles (small values)
-                idxs = np.argwhere(image.sum(axis=(1, 2, 3)) < thresh)
-                image = image[idxs.reshape(-1)]
+                idxs = np.argwhere(img_np.sum(axis=(1, 2, 3)) < thresh)
+                img_np = img_np[idxs.reshape(-1)]
             elif self.filter_mode == "max":
-                idxs = np.argwhere(image.sum(axis=(1, 2, 3)) >= thresh)
-                image = image[idxs.reshape(-1)]
+                idxs = np.argwhere(img_np.sum(axis=(1, 2, 3)) >= thresh)
+                img_np = img_np[idxs.reshape(-1)]
 
         else:
-            if len(image) > self.tile_count:
+            if len(img_np) > self.tile_count:
 
                 if self.filter_mode == "min":
                     # default, keep non-background tiles (smallest values)
-                    idxs = np.argsort(image.sum(axis=(1, 2, 3)))[: self.tile_count]
-                    image = image[idxs]
+                    idxs = np.argsort(img_np.sum(axis=(1, 2, 3)))[: self.tile_count]
+                    img_np = img_np[idxs]
                 elif self.filter_mode == "max":
-                    idxs = np.argsort(image.sum(axis=(1, 2, 3)))[-self.tile_count :]
-                    image = image[idxs]
+                    idxs = np.argsort(img_np.sum(axis=(1, 2, 3)))[-self.tile_count :]
+                    img_np = img_np[idxs]
                 else:
                     # random subset (more appropriate for WSIs without distinct background)
                     if self.random_idxs is not None:
-                        image = image[self.random_idxs]
+                        img_np = img_np[self.random_idxs]
 
-            elif len(image) < self.tile_count:
-                image = np.pad(
-                    image,
-                    [[0, self.tile_count - len(image)], [0, 0], [0, 0], [0, 0]],
+            elif len(img_np) < self.tile_count:
+                img_np = np.pad(
+                    img_np,
+                    [[0, self.tile_count - len(img_np)], [0, 0], [0, 0], [0, 0]],
                     constant_values=self.background_val,
                 )
 
+        image, *_ = convert_to_dst_type(src=img_np, dst=image, dtype=image.dtype)
+
         return image
diff --git a/monai/apps/pathology/transforms/spatial/dictionary.py b/monai/apps/pathology/transforms/spatial/dictionary.py
--- a/monai/apps/pathology/transforms/spatial/dictionary.py
+++ b/monai/apps/pathology/transforms/spatial/dictionary.py
@@ -12,8 +12,6 @@
 import copy
 from typing import Any, Dict, Hashable, List, Mapping, Optional, Tuple, Union
 
-import numpy as np
-
 from monai.config import KeysCollection
 from monai.config.type_definitions import NdarrayOrTensor
 from monai.transforms.transform import MapTransform, Randomizable
@@ -81,6 +79,8 @@ class TileOnGridd(Randomizable, MapTransform):
 
     """
 
+    backend = SplitOnGrid.backend
+
     def __init__(
         self,
         keys: KeysCollection,
@@ -112,7 +112,9 @@ def __init__(
     def randomize(self, data: Any = None) -> None:
         self.seed = self.R.randint(10000)  # type: ignore
 
-    def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Union[Dict[Hashable, np.ndarray], List[Dict]]:
+    def __call__(
+        self, data: Mapping[Hashable, NdarrayOrTensor]
+    ) -> Union[Dict[Hashable, NdarrayOrTensor], List[Dict[Hashable, NdarrayOrTensor]]]:
 
         self.randomize()
 


Test patch:
diff --git a/tests/test_tile_on_grid.py b/tests/test_tile_on_grid.py
--- a/tests/test_tile_on_grid.py
+++ b/tests/test_tile_on_grid.py
@@ -16,6 +16,7 @@
 from parameterized import parameterized
 
 from monai.apps.pathology.transforms import TileOnGrid
+from tests.utils import TEST_NDARRAYS, assert_allclose
 
 TEST_CASES = []
 for tile_count in [16, 64]:
@@ -38,6 +39,10 @@
     for step in [4, 8]:
         TEST_CASES.append([{"tile_count": 16, "step": step, "tile_size": tile_size}])
 
+TESTS = []
+for p in TEST_NDARRAYS:
+    for tc in TEST_CASES:
+        TESTS.append([p, *tc])
 
 TEST_CASES2 = []
 for tile_count in [16, 64]:
@@ -56,6 +61,11 @@
                     ]
                 )
 
+TESTS2 = []
+for p in TEST_NDARRAYS:
+    for tc in TEST_CASES2:
+        TESTS2.append([p, *tc])
+
 
 def make_image(
     tile_count: int,
@@ -104,25 +114,27 @@ def make_image(
 
 
 class TestTileOnGrid(unittest.TestCase):
-    @parameterized.expand(TEST_CASES)
-    def test_tile_patch_single_call(self, input_parameters):
+    @parameterized.expand(TESTS)
+    def test_tile_patch_single_call(self, in_type, input_parameters):
 
         img, tiles = make_image(**input_parameters)
+        input_img = in_type(img)
 
         tiler = TileOnGrid(**input_parameters)
-        output = tiler(img)
-        np.testing.assert_equal(output, tiles)
+        output = tiler(input_img)
+        assert_allclose(output, tiles, type_test=False)
 
-    @parameterized.expand(TEST_CASES2)
-    def test_tile_patch_random_call(self, input_parameters):
+    @parameterized.expand(TESTS2)
+    def test_tile_patch_random_call(self, in_type, input_parameters):
 
         img, tiles = make_image(**input_parameters, seed=123)
+        input_img = in_type(img)
 
         tiler = TileOnGrid(**input_parameters)
         tiler.set_random_state(seed=123)
 
-        output = tiler(img)
-        np.testing.assert_equal(output, tiles)
+        output = tiler(input_img)
+        assert_allclose(output, tiles, type_test=False)
 
 
 if __name__ == "__main__":
diff --git a/tests/test_tile_on_grid_dict.py b/tests/test_tile_on_grid_dict.py
--- a/tests/test_tile_on_grid_dict.py
+++ b/tests/test_tile_on_grid_dict.py
@@ -13,9 +13,11 @@
 from typing import Optional
 
 import numpy as np
+import torch
 from parameterized import parameterized
 
 from monai.apps.pathology.transforms import TileOnGridDict
+from tests.utils import TEST_NDARRAYS, assert_allclose
 
 TEST_CASES = []
 for tile_count in [16, 64]:
@@ -36,11 +38,14 @@
                         ]
                     )
 
-
 for tile_size in [8, 16]:
     for step in [4, 8]:
         TEST_CASES.append([{"tile_count": 16, "step": step, "tile_size": tile_size}])
 
+TESTS = []
+for p in TEST_NDARRAYS:
+    for tc in TEST_CASES:
+        TESTS.append([p, *tc])
 
 TEST_CASES2 = []
 for tile_count in [16, 64]:
@@ -61,6 +66,10 @@
                         ]
                     )
 
+TESTS2 = []
+for p in TEST_NDARRAYS:
+    for tc in TEST_CASES2:
+        TESTS2.append([p, *tc])
 
 for tile_size in [8, 16]:
     for step in [4, 8]:
@@ -114,27 +123,31 @@ def make_image(
 
 
 class TestTileOnGridDict(unittest.TestCase):
-    @parameterized.expand(TEST_CASES)
-    def test_tile_patch_single_call(self, input_parameters):
+    @parameterized.expand(TESTS)
+    def test_tile_patch_single_call(self, in_type, input_parameters):
 
         key = "image"
         input_parameters["keys"] = key
 
         img, tiles = make_image(**input_parameters)
+        input_img = in_type(img)
 
         splitter = TileOnGridDict(**input_parameters)
 
-        output = splitter({key: img})
+        output = splitter({key: input_img})
 
         if input_parameters.get("return_list_of_dicts", False):
-            output = np.stack([ix[key] for ix in output], axis=0)
+            if isinstance(input_img, torch.Tensor):
+                output = torch.stack([ix[key] for ix in output], axis=0)
+            else:
+                output = np.stack([ix[key] for ix in output], axis=0)
         else:
             output = output[key]
 
-        np.testing.assert_equal(tiles, output)
+        assert_allclose(output, tiles, type_test=False)
 
-    @parameterized.expand(TEST_CASES2)
-    def test_tile_patch_random_call(self, input_parameters):
+    @parameterized.expand(TESTS2)
+    def test_tile_patch_random_call(self, in_type, input_parameters):
 
         key = "image"
         input_parameters["keys"] = key
@@ -142,18 +155,21 @@ def test_tile_patch_random_call(self, input_parameters):
         random_state = np.random.RandomState(123)
         seed = random_state.randint(10000)
         img, tiles = make_image(**input_parameters, seed=seed)
+        input_img = in_type(img)
 
         splitter = TileOnGridDict(**input_parameters)
         splitter.set_random_state(seed=123)
 
-        output = splitter({key: img})
+        output = splitter({key: input_img})
 
         if input_parameters.get("return_list_of_dicts", False):
-            output = np.stack([ix[key] for ix in output], axis=0)
+            if isinstance(input_img, torch.Tensor):
+                output = torch.stack([ix[key] for ix in output], axis=0)
+            else:
+                output = np.stack([ix[key] for ix in output], axis=0)
         else:
             output = output[key]
-
-        np.testing.assert_equal(tiles, output)
+        assert_allclose(output, tiles, type_test=False)
 
 
 if __name__ == "__main__":