prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  AttributeError: '_OpNamespace' 'torchvision' object has no attribute 'roi_align' 
  Hi, I'm getting the following error while running the e2e tests in Torch-MLIR. I'm trying to get a small reproducible for the issue. Meanwhile, if someone is familiar with the error or knows what could be the probable reason behind this, it would be helpful to know. Some changes have been made here: https://github.com/pytorch/vision/commit/8f2e5c90ce0f55877eddb1f7fee8f8b48004849b, but I'm not sure how this relates to the below error.
  
  ```
  Traceback (most recent call last): 
  
  38535 File "/main_checkout/torch-mlir/docker_venv/lib/python3.10/site-packages/torch/_ops.py", line 705, in __getattr__ 
  
  38536 op, overload_names = torch._C._jit_get_operation(qualified_op_name) 
  
  38537RuntimeError: No such operator torchvision::roi_align 
  
  38538 
  
  38539The above exception was the direct cause of the following exception: 
  
  38540 
  
  38541Traceback (most recent call last): 
  
  38542 File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main 
  
  38543 return _run_code(code, main_globals, None, 
  
  38544 File "/usr/lib/python3.10/runpy.py", line 86, in _run_code 
  
  38545 exec(code, run_globals) 
  
  38546 File "/main_checkout/torch-mlir/e2e_testing/main.py", line 34, in <module> 
  
  38547 register_all_tests() 
  
  38548 File "/main_checkout/torch-mlir/build/tools/torch-mlir/python_packages/torch_mlir/torch_mlir_e2e_test/test_suite/__init__.py", line 18, in register_all_tests 
  
  38549 from . import vision_models 
  
  38550 File "/main_checkout/torch-mlir/build/tools/torch-mlir/python_packages/torch_mlir/torch_mlir_e2e_test/test_suite/vision_models.py", line 7, in <module> 
  
  38551 import torchvision.models as models 
  
  38552 File "/main_checkout/torch-mlir/docker_venv/lib/python3.10/site-packages/torchvision/__init__.py", line 6, in <module> 
  
  38553 from torchvision import _meta_registrations, datasets, io, models, ops, transforms, utils 
  
  38554 File "/main_checkout/torch-mlir/docker_venv/lib/python3.10/site-packages/torchvision/_meta_registrations.py", line 22, in <module> 
  
  38555 @register_meta(vision.roi_align.default) 
  
  38556 File "/main_checkout/torch-mlir/docker_venv/lib/python3.10/site-packages/torch/_ops.py", line 709, in __getattr__ 
  
  38557 raise AttributeError( 
  
  38558AttributeError: '_OpNamespace' 'torchvision' object has no attribute 'roi_align' 
  
  38559Error: Process completed with exit code 1.
  ```
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `torchvision/_meta_registrations.py`
  Content:
  ```
  1 import torch
  2 import torch.library
  3 
  4 # Ensure that torch.ops.torchvision is visible
  5 import torchvision.extension  # noqa: F401
  6 
  7 from torch._prims_common import check
  8 
  9 _meta_lib = torch.library.Library("torchvision", "IMPL", "Meta")
  10 
  11 vision = torch.ops.torchvision
  12 
  13 
  14 def register_meta(op):
  15     def wrapper(fn):
  16         _meta_lib.impl(op, fn)
  17         return fn
  18 
  19     return wrapper
  20 
  21 
  22 @register_meta(vision.roi_align.default)
  23 def meta_roi_align(input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio, aligned):
  24     check(rois.size(1) == 5, lambda: "rois must have shape as Tensor[K, 5]")
  25     check(
  26         input.dtype == rois.dtype,
  27         lambda: (
  28             "Expected tensor for input to have the same type as tensor for rois; "
  29             f"but type {input.dtype} does not equal {rois.dtype}"
  30         ),
  31     )
  32     num_rois = rois.size(0)
  33     _, channels, height, width = input.size()
  34     return input.new_empty((num_rois, channels, pooled_height, pooled_width))
  35 
  36 
  37 @register_meta(vision._roi_align_backward.default)
  38 def meta_roi_align_backward(
  39     grad, rois, spatial_scale, pooled_height, pooled_width, batch_size, channels, height, width, sampling_ratio, aligned
  40 ):
  41     check(
  42         grad.dtype == rois.dtype,
  43         lambda: (
  44             "Expected tensor for grad to have the same type as tensor for rois; "
  45             f"but type {grad.dtype} does not equal {rois.dtype}"
  46         ),
  47     )
  48     return grad.new_empty((batch_size, channels, height, width))
  49
  ```
  --- END FILES ---
  
  Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.
  
  Here is an example:
  
  ```diff
  diff --git a/examples/server_async.py b/examples/server_async.py
  --- a/examples/server_async.py
  +++ b/examples/server_async.py
  @@ -313,4 +313,4 @@
   
   
   if __name__ == "__main__":
  -    asyncio.run(run_async_server("."), debug=True)
  +    asyncio.run(run_async_server(), debug=True)
  diff --git a/examples/server_sync.py b/examples/server_sync.py
  --- a/examples/server_sync.py
  +++ b/examples/server_sync.py
  @@ -313,5 +313,5 @@
   
   
   if __name__ == "__main__":
  -    server = run_sync_server(".")
  +    server = run_sync_server()
       server.shutdown()
  
  ```
  
golden_diff: diff --git a/torchvision/_meta_registrations.py b/torchvision/_meta_registrations.py
  --- a/torchvision/_meta_registrations.py
  +++ b/torchvision/_meta_registrations.py
  @@ -1,3 +1,5 @@
  +import functools
  +
   import torch
   import torch.library
   
  @@ -6,20 +8,22 @@
   
   from torch._prims_common import check
   
  -_meta_lib = torch.library.Library("torchvision", "IMPL", "Meta")
   
  -vision = torch.ops.torchvision
  +@functools.lru_cache(None)
  +def get_meta_lib():
  +    return torch.library.Library("torchvision", "IMPL", "Meta")
   
   
  -def register_meta(op):
  +def register_meta(op_name, overload_name="default"):
       def wrapper(fn):
  -        _meta_lib.impl(op, fn)
  +        if torchvision.extension._has_ops():
  +            get_meta_lib().impl(getattr(getattr(torch.ops.torchvision, op_name), overload_name), fn)
           return fn
   
       return wrapper
   
   
  -@register_meta(vision.roi_align.default)
  +@register_meta("roi_align")
   def meta_roi_align(input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio, aligned):
       check(rois.size(1) == 5, lambda: "rois must have shape as Tensor[K, 5]")
       check(
  @@ -34,7 +38,7 @@
       return input.new_empty((num_rois, channels, pooled_height, pooled_width))
   
   
  -@register_meta(vision._roi_align_backward.default)
  +@register_meta("_roi_align_backward")
   def meta_roi_align_backward(
       grad, rois, spatial_scale, pooled_height, pooled_width, batch_size, channels, height, width, sampling_ratio, aligned
   ):
  
verification_info: {"golden_diff": "diff --git a/torchvision/_meta_registrations.py b/torchvision/_meta_registrations.py\n--- a/torchvision/_meta_registrations.py\n+++ b/torchvision/_meta_registrations.py\n@@ -1,3 +1,5 @@\n+import functools\n+\n import torch\n import torch.library\n \n@@ -6,20 +8,22 @@\n \n from torch._prims_common import check\n \n-_meta_lib = torch.library.Library(\"torchvision\", \"IMPL\", \"Meta\")\n \n-vision = torch.ops.torchvision\n+@functools.lru_cache(None)\n+def get_meta_lib():\n+    return torch.library.Library(\"torchvision\", \"IMPL\", \"Meta\")\n \n \n-def register_meta(op):\n+def register_meta(op_name, overload_name=\"default\"):\n     def wrapper(fn):\n-        _meta_lib.impl(op, fn)\n+        if torchvision.extension._has_ops():\n+            get_meta_lib().impl(getattr(getattr(torch.ops.torchvision, op_name), overload_name), fn)\n         return fn\n \n     return wrapper\n \n \n-@register_meta(vision.roi_align.default)\n+@register_meta(\"roi_align\")\n def meta_roi_align(input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio, aligned):\n     check(rois.size(1) == 5, lambda: \"rois must have shape as Tensor[K, 5]\")\n     check(\n@@ -34,7 +38,7 @@\n     return input.new_empty((num_rois, channels, pooled_height, pooled_width))\n \n \n-@register_meta(vision._roi_align_backward.default)\n+@register_meta(\"_roi_align_backward\")\n def meta_roi_align_backward(\n     grad, rois, spatial_scale, pooled_height, pooled_width, batch_size, channels, height, width, sampling_ratio, aligned\n ):\n", "issue": "AttributeError: '_OpNamespace' 'torchvision' object has no attribute 'roi_align' \nHi, I'm getting the following error while running the e2e tests in Torch-MLIR. I'm trying to get a small reproducible for the issue. Meanwhile, if someone is familiar with the error or knows what could be the probable reason behind this, it would be helpful to know. Some changes have been made here: https://github.com/pytorch/vision/commit/8f2e5c90ce0f55877eddb1f7fee8f8b48004849b, but I'm not sure how this relates to the below error.\n\n```\nTraceback (most recent call last): \n\n38535 File \"/main_checkout/torch-mlir/docker_venv/lib/python3.10/site-packages/torch/_ops.py\", line 705, in __getattr__ \n\n38536 op, overload_names = torch._C._jit_get_operation(qualified_op_name) \n\n38537RuntimeError: No such operator torchvision::roi_align \n\n38538 \n\n38539The above exception was the direct cause of the following exception: \n\n38540 \n\n38541Traceback (most recent call last): \n\n38542 File \"/usr/lib/python3.10/runpy.py\", line 196, in _run_module_as_main \n\n38543 return _run_code(code, main_globals, None, \n\n38544 File \"/usr/lib/python3.10/runpy.py\", line 86, in _run_code \n\n38545 exec(code, run_globals) \n\n38546 File \"/main_checkout/torch-mlir/e2e_testing/main.py\", line 34, in <module> \n\n38547 register_all_tests() \n\n38548 File \"/main_checkout/torch-mlir/build/tools/torch-mlir/python_packages/torch_mlir/torch_mlir_e2e_test/test_suite/__init__.py\", line 18, in register_all_tests \n\n38549 from . import vision_models \n\n38550 File \"/main_checkout/torch-mlir/build/tools/torch-mlir/python_packages/torch_mlir/torch_mlir_e2e_test/test_suite/vision_models.py\", line 7, in <module> \n\n38551 import torchvision.models as models \n\n38552 File \"/main_checkout/torch-mlir/docker_venv/lib/python3.10/site-packages/torchvision/__init__.py\", line 6, in <module> \n\n38553 from torchvision import _meta_registrations, datasets, io, models, ops, transforms, utils \n\n38554 File \"/main_checkout/torch-mlir/docker_venv/lib/python3.10/site-packages/torchvision/_meta_registrations.py\", line 22, in <module> \n\n38555 @register_meta(vision.roi_align.default) \n\n38556 File \"/main_checkout/torch-mlir/docker_venv/lib/python3.10/site-packages/torch/_ops.py\", line 709, in __getattr__ \n\n38557 raise AttributeError( \n\n38558AttributeError: '_OpNamespace' 'torchvision' object has no attribute 'roi_align' \n\n38559Error: Process completed with exit code 1.\n```\n", "before_files": [{"content": "import torch\nimport torch.library\n\n# Ensure that torch.ops.torchvision is visible\nimport torchvision.extension  # noqa: F401\n\nfrom torch._prims_common import check\n\n_meta_lib = torch.library.Library(\"torchvision\", \"IMPL\", \"Meta\")\n\nvision = torch.ops.torchvision\n\n\ndef register_meta(op):\n    def wrapper(fn):\n        _meta_lib.impl(op, fn)\n        return fn\n\n    return wrapper\n\n\n@register_meta(vision.roi_align.default)\ndef meta_roi_align(input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio, aligned):\n    check(rois.size(1) == 5, lambda: \"rois must have shape as Tensor[K, 5]\")\n    check(\n        input.dtype == rois.dtype,\n        lambda: (\n            \"Expected tensor for input to have the same type as tensor for rois; \"\n            f\"but type {input.dtype} does not equal {rois.dtype}\"\n        ),\n    )\n    num_rois = rois.size(0)\n    _, channels, height, width = input.size()\n    return input.new_empty((num_rois, channels, pooled_height, pooled_width))\n\n\n@register_meta(vision._roi_align_backward.default)\ndef meta_roi_align_backward(\n    grad, rois, spatial_scale, pooled_height, pooled_width, batch_size, channels, height, width, sampling_ratio, aligned\n):\n    check(\n        grad.dtype == rois.dtype,\n        lambda: (\n            \"Expected tensor for grad to have the same type as tensor for rois; \"\n            f\"but type {grad.dtype} does not equal {rois.dtype}\"\n        ),\n    )\n    return grad.new_empty((batch_size, channels, height, width))\n", "path": "torchvision/_meta_registrations.py"}], "after_files": [{"content": "import functools\n\nimport torch\nimport torch.library\n\n# Ensure that torch.ops.torchvision is visible\nimport torchvision.extension  # noqa: F401\n\nfrom torch._prims_common import check\n\n\n@functools.lru_cache(None)\ndef get_meta_lib():\n    return torch.library.Library(\"torchvision\", \"IMPL\", \"Meta\")\n\n\ndef register_meta(op_name, overload_name=\"default\"):\n    def wrapper(fn):\n        if torchvision.extension._has_ops():\n            get_meta_lib().impl(getattr(getattr(torch.ops.torchvision, op_name), overload_name), fn)\n        return fn\n\n    return wrapper\n\n\n@register_meta(\"roi_align\")\ndef meta_roi_align(input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio, aligned):\n    check(rois.size(1) == 5, lambda: \"rois must have shape as Tensor[K, 5]\")\n    check(\n        input.dtype == rois.dtype,\n        lambda: (\n            \"Expected tensor for input to have the same type as tensor for rois; \"\n            f\"but type {input.dtype} does not equal {rois.dtype}\"\n        ),\n    )\n    num_rois = rois.size(0)\n    _, channels, height, width = input.size()\n    return input.new_empty((num_rois, channels, pooled_height, pooled_width))\n\n\n@register_meta(\"_roi_align_backward\")\ndef meta_roi_align_backward(\n    grad, rois, spatial_scale, pooled_height, pooled_width, batch_size, channels, height, width, sampling_ratio, aligned\n):\n    check(\n        grad.dtype == rois.dtype,\n        lambda: (\n            \"Expected tensor for grad to have the same type as tensor for rois; \"\n            f\"but type {grad.dtype} does not equal {rois.dtype}\"\n        ),\n    )\n    return grad.new_empty((batch_size, channels, height, width))\n", "path": "torchvision/_meta_registrations.py"}]}