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[ "Hi, @goruck \r\n\r\nIt seems like you're trying to use legacy Keras code which is stale and about to be deleted. we have added warning on [that page](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/python/keras#stop) and The current Keras code lives in [github/keras-team/keras](https://github.com/keras-team/keras).\r\n\r\nWe have also updated in our `TensorFlow 2.11.0` [release notes](https://github.com/tensorflow/tensorflow/releases#:~:text=tensorflow/python/keras%20code%20is%20a%20legacy%20copy%20of%20Keras%20since%20the%20TensorFlow%20v2.7%20release%2C%20and%20will%20be%20deleted%20in%20the%20v2.12%20release.%20Please%20remove%20any%20import%20of%20tensorflow.python.keras%20and%20use%20the%20public%20API%20with%20from%20tensorflow%20import%20keras%20or%20import%20tensorflow%20as%20tf%3B%20tf.keras.) and you'll find `.py `files in the `normalization` folder after clicking on this [link](https://github.com/keras-team/keras)( keras->layers->normalization) and please use the public API with `from tensorflow import keras` as shown in this [gist file](https://colab.research.google.com/gist/gaikwadrahul8/0bbf4b93862ab9e1410845bc4e94c7d4/-59612.ipynb) and I was able to replicate the issue on `ubuntu 20.04` with `Tensorflow==2.11` and it's importing as expected, I have added screenshot for your reference below:\r\n\r\n\r\n\r\nIf issue still persists please let us know? or Could you please confirm if this issue is resolved for you ? Please feel free to close the issue if it is resolved ? Thank you!", "Hi @gaikwadrahul8, this resolved it perfectly. Thank you so much!" ]

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[ "@mazeltovlee \r\nI was able to reproduce the issue on Colab using TF v2.11 and tf-nightly. Please find the gist of [TF v2.11](https://colab.research.google.com/gist/tiruk007/104184ad07c155074f46055be5db683f/untitled117.ipynb) and [tf_nightly-2.13.0.dev20230212](https://colab.research.google.com/gist/tiruk007/c35fe01535a05b5c259ec9a384566714/untitled118.ipynb) for reference.It seems like we have to dig more into this issue, we will update soon here. \r\n\r\nThank you!", "tf.math.tan seems to be able to accept integer values in a tensor as long as one or more non integer values are present.", "@mazeltovlee ,\r\n\r\nThanks for reporting this. There is some conflict here.I can see from file `tensorflow.python.ops.gen_math_ops` where `tan() `is accepting the args where `int` dtype is not in that list which is conflict to what documentation says. Need to check the code at C++ level for exact supported `dtypes` and will do necessary changes to documentation/code if needed any.\r\n\r\n<img width=\"1457\" alt=\"Screenshot 2023-02-20 at 12 41 05 PM\" src=\"https://user-images.githubusercontent.com/116063290/220041333-19ab27ce-55f7-4e60-8f09-71838341dd78.png\">\r\n\r\n\r\nBut please note that there is no Crash but only user error as Invalid Argument Error.Could you actually find crash apart from the Invalid Argument Error? \r\n", "Hi @SuryanarayanaY ,\r\n\r\nThe error I am experiencing is the NotFoundError:\r\n\r\n```\r\ntensorflow.python.framework.errors_impl.NotFoundError: Could not find device for node: {{node Tan}} = Tan[T=DT_INT8]\r\nAll kernels registered for op Tan:\r\n device='XLA_CPU_JIT'; T in [DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT16, DT_INT8, DT_COMPLEX64, DT_INT64, DT_BFLOAT16, DT_COMPLEX128, DT_HALF]\r\n device='XLA_GPU_JIT'; T in [DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT16, DT_INT8, DT_COMPLEX64, DT_INT64, DT_BFLOAT16, DT_COMPLEX128, DT_HALF]\r\n device='CPU'; T in [DT_COMPLEX128]\r\n device='CPU'; T in [DT_COMPLEX64]\r\n device='CPU'; T in [DT_DOUBLE]\r\n device='CPU'; T in [DT_FLOAT]\r\n device='CPU'; T in [DT_BFLOAT16]\r\n device='CPU'; T in [DT_HALF]\r\n device='GPU'; T in [DT_COMPLEX128]\r\n device='GPU'; T in [DT_COMPLEX64]\r\n device='GPU'; T in [DT_DOUBLE]\r\n device='GPU'; T in [DT_FLOAT]\r\n device='GPU'; T in [DT_HALF]\r\n [Op:Tan]\r\n```\r\nI find no other error message apart from this one.", "@justacookiee , If the Tensor contains at least one float value then all other ints in the Tensor also considered as float only.\r\n\r\n@mazeltovlee , As per error message there is supported kernel for this Op with \"XLA_CPU_JIT\" and also \"XLA_GPU_JIT\". Hence I tried to check this API wrapped under `tf.function` with` jit_compile=True` then also the API is not accepting the Integers.Please refer to attached [gist](https://colab.research.google.com/gist/SuryanarayanaY/96d7dcbec9d432900e76bd5436807b0f/59611.ipynb).", "As per the source code:\r\n\r\n Supported operators for device: XLA_GPU_JIT\r\n\r\n\r\n\r\n> Tan | T={complex64,double,float,int32,int64}\r\n> -- | --\r\n\r\n\r\n\r\n\r\nSupported operators for device: XLA_CPU_JIT\r\n\r\n\r\n> \r\n> Tan | T={complex64,double,float,int32,int64}\r\n> -- | --\r\n\r\n\r\n\r\n\r\n", "Hi @mazeltovlee , Interger dtype was never intended to be supported for `Tan`, the fix has been made and submitted in the commit here https://github.com/tensorflow/tensorflow/commit/fc0095d3ee7dd21b6b61978a8dd2f526c7455605#diff-85cdc6a4e3fb58b63753cb91f5c637e489b4fa481a7e848afe0c02f026181536, showing the supported dtypes.\r\nDocument will be updated inline to the above commit once the Tensorflow 2.12 release is published. Thanks!", "Thanks for the fix and the explanation. I am closing this issue.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59611\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59611\">No</a>\n" ]

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[ "close it as https://github.com/tensorflow/tensorflow/commit/e9bd1094b7d3c047ff6abbe503e124f253ea29ce has fixed it\r\n\r\nThanks! @cheshire " ]

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[ "Hi @AbhisekOmkar It looks like your PR relates to the Keras component. Please submit it to the github.com/keras-team/keras repository instead. Thankyou.\r\n@fchollet, @qlzh727" ]

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[ "it is related to intializers in general - https://github.com/tensorflow/tensorflow/issues/57763 . Resolving this issue.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59608\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59608\">No</a>\n" ]

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[ "Since #59606 was merged, this is ready for review :)", "cc @rsuderman @jpienaar", "(I don't know why AMD build failed here)", "friendly ping if you have some free time :) - the AMD build failure doesn't look related" ]

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[ "@NatashaKnk for visibility given you are looking close to here.", "Hi, it looks like something failed CI, but because its an internal check I wasn't able to take a look: `Google internal checks FAILED for runs with create time 2023-02-20T18:50`, is there a way I can view more information about the error?", "I see a failure on tensorflow/compiler/mlir/tosa/tests:tfl-to-tosa-pipeline.mlir.test , could you check locally?", "Hmm they seem to be passing for me locally, even with the branch rebased on `e6177f7d`", "I'll check later but the error was around a non-integer/float type being queried from wrong attribute. Could you check with assertions enabled? (I think for this tool you could still build the debug version).", "Thanks @jpienaar, building in debug revealed the issue.. hoping its fixed now!" ]

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[ "Thanks for the feature request. AFAIK, we don't have anything planned for the minimal tensorflow version to reduce the dependency packages.\r\n@learning-to-play , Could you please take a look into this request.\r\n" ]

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[ "#0 0x0000007ff7d01710 in strlen () from /lib/aarch64-linux-gnu/libc.so.6\r\nhttps://github.com/google-coral/edgetpu/issues/1 0x0000007ff5ac1264 in tflite::InterpreterBuilder::ParseSignatureDefs(flatbuffers::Vector<flatbuffers::Offsettflite::SignatureDef > const*, tflite::Interpreter*) ()\r\nfrom /usr/lib/python3/dist-packages/tflite_runtime/_pywrap_tensorflow_interpreter_wrapper.cpython-37m-aarch64-linux-gnu.so\r\nhttps://github.com/google-coral/edgetpu/pull/2 0x0000007ff5ac19d4 in tflite::InterpreterBuilder::operator()(std::unique_ptr<tflite::Interpreter, std::default_deletetflite::Interpreter >, int) ()\r\nfrom /usr/lib/python3/dist-packages/tflite_runtime/_pywrap_tensorflow_interpreter_wrapper.cpython-37m-aarch64-linux-gnu.so\r\nhttps://github.com/google-coral/edgetpu/issues/3 0x0000007ff58f3440 in tflite::interpreter_wrapper::InterpreterWrapper::CreateInterpreterWrapper(std::unique_ptr<tflite::FlatBufferModel, std::default_deletetflite::FlatBufferModel >, std::unique_ptr<tflite::interpreter_wrapper::PythonErrorReporter, std::default_deletetflite::interpreter_wrapper::PythonErrorReporter >, std::vector<std::__cxx11::basic_string<char, std::char_traits, std::allocator >, std::allocator<std::__cxx11::basic_string<char, std::char_traits, std::allocator > > > const&, std::vector<std::function<void (unsigned long)>, std::allocator<std::function<void (unsigned long)> > > const&, std::__cxx11::basic_string<char, std::char_traits, std::allocator >) ()\r\nfrom /usr/lib/python3/dist-packages/tflite_runtime/_pywrap_tensorflow_interpreter_wrapper.cpython-37m-aarch64-linux-gnu.so\r\nhttps://github.com/google-coral/edgetpu/issues/4 0x0000007ff58f39c0 in tflite::interpreter_wrapper::InterpreterWrapper::CreateWrapperCPPFromFile(char const*, std::vector<std::__cxx11::basic_string<char, std::char_traits, std::allocator >, std::allocator<std::__cxx11::basic_string<char, std::char_traits, std::allocator > > > const&, std::vector<std::function<void (unsigned long)>, std::allocator<std::function<void (unsigned long)> > > const&, std::__cxx11::basic_string<char, std::char_traits, std::allocator >) ()\r\nfrom /usr/lib/python3/dist-packages/tflite_runtime/pywrap_tensorflow_interpreter_wrapper.cpython-37m-aarch64-linux-gnu.so\r\nhttps://github.com/google-coral/edgetpu/issues/5 0x0000007ff58f08d0 in pybind11::cpp_function::initialize<pybind11_init__pywrap_tensorflow_interpreter_wrapper(pybind11::module&)::{lambda(std::__cxx11::basic_string<char, std::char_traits, std::allocator > const&, std::vector<std::__cxx11::basic_string<char, std::char_traits, std::allocator >, std::allocator<std::__cxx11::basic_string<char, std::char_traits, std::allocator > > > const&, std::vector<std::function<void (unsigned long--Type for more, q to quit, c to continue without paging--\r\n)>, std::allocator<std::function<void (unsigned long)> > > const&)https://github.com/google-coral/edgetpu/pull/2}, tflite::interpreter_wrapper::InterpreterWrapper, std::__cxx11::basic_string<char, std::char_traits, std::allocator > const&, std::vector<std::__cxx11::basic_string<char, std::char_traits, std::allocator >, std::allocator<std::_cxx11::basic_string<char, std::char_traits, std::allocator > > > const&, std::vector<std::function<void (unsigned long)>, std::allocator<std::function<void (unsigned long)> > > const&, pybind11::name, pybind11::scope, pybind11::sibling>(pybind11_init__pywrap_tensorflow_interpreter_wrapper(pybind11::module&)::{lambda(std::__cxx11::basic_string<char, std::char_traits, std::allocator > const&, std::vector<std::__cxx11::basic_string<char, std::char_traits, std::allocator >, std::allocator<std::__cxx11::basic_string<char, std::char_traits, std::allocator > > > const&, std::vector<std::function<void (unsigned long)>, std::allocator<std::function<void (unsigned long)> > > const&)https://github.com/google-coral/edgetpu/pull/2}&&, tflite::interpreter_wrapper::InterpreterWrapper* ()(std::__cxx11::basic_string<char, std::char_traits, std::allocator > const&, std::vector<std::__cxx11::basic_string<char, std::char_traits, std::allocator >, std::allocator<std::__cxx11::basic_string<char, std::char_traits, std::allocator > > > const&, std::vector<std::function<void (unsigned long)>, std::allocator<std::function<void (unsigned long)> > > const&), pybind11::name const&, pybind11::scope const&, pybind11::sibling const&)::{lambda(pybind11::detail::function_call&)https://github.com/google-coral/edgetpu/issues/3}::_FUN(pybind11::detail::function_call) ()\r\nfrom /usr/lib/python3/dist-packages/tflite_runtime/_pywrap_tensorflow_interpreter_wrapper.cpython-37m-aarch64-linux-gnu.so\r\nhttps://github.com/google-coral/edgetpu/issues/6 0x0000007ff58e78ec in pybind11::cpp_function::dispatcher(_object, _object*, _object*) ()\r\nfrom /usr/lib/python3/dist-packages/tflite_runtime/_pywrap_tensorflow_interpreter_wrapper.cpython-37m-aarch64-linux-gnu.so\r\nhttps://github.com/google-coral/edgetpu/pull/7 0x000000000043cf90 in _PyMethodDef_RawFastCallKeywords ()\r\nhttps://github.com/google-coral/edgetpu/issues/8 0x000000000043d030 in _PyCFunction_FastCallKeywords ()\r\nhttps://github.com/google-coral/edgetpu/issues/9 0x000000000042b0e4 in _PyEval_EvalFrameDefault ()\r\nhttps://github.com/google-coral/edgetpu/issues/10 0x00000000004df2bc in _PyEval_EvalCodeWithName ()\r\nhttps://github.com/google-coral/edgetpu/issues/11 0x000000000043c9bc in _PyFunction_FastCallDict ()\r\nhttps://github.com/google-coral/edgetpu/issues/12 0x000000000043e104 in _PyObject_Call_Prepend ()\r\nhttps://github.com/google-coral/edgetpu/issues/13 0x000000000048e078 in ?? ()\r\nhttps://github.com/google-coral/edgetpu/issues/14 0x0000000000487858 in ?? ()\r\nhttps://github.com/google-coral/edgetpu/issues/15 0x000000000043d240 in _PyObject_FastCallKeywords ()\r\n--Type for more, q to quit, c to continue without paging--\r\nhttps://github.com/google-coral/edgetpu/issues/16 0x0000000000425490 in _PyEval_EvalFrameDefault ()\r\nhttps://github.com/google-coral/edgetpu/issues/17 0x00000000004df2bc in _PyEval_EvalCodeWithName ()\r\nhttps://github.com/google-coral/edgetpu/issues/18 0x000000000043cb24 in _PyFunction_FastCallKeywords ()\r\nhttps://github.com/google-coral/edgetpu/issues/19 0x0000000000429e78 in _PyEval_EvalFrameDefault ()\r\nhttps://github.com/google-coral/edgetpu/issues/20 0x0000000000422914 in ?? ()\r\nhttps://github.com/google-coral/edgetpu/issues/21 0x0000000000429bac in _PyEval_EvalFrameDefault ()\r\nhttps://github.com/google-coral/edgetpu/issues/22 0x00000000004df2bc in _PyEval_EvalCodeWithName ()\r\nhttps://github.com/google-coral/edgetpu/issues/23 0x00000000004df600 in PyEval_EvalCode ()\r\nhttps://github.com/google-coral/edgetpu/issues/24 0x0000000000512430 in PyRun_FileExFlags ()\r\nhttps://github.com/google-coral/edgetpu/issues/25 0x000000000051260c in PyRun_SimpleFileExFlags ()\r\nhttps://github.com/google-coral/edgetpu/issues/26 0x0000000000431be8 in ?? ()\r\nhttps://github.com/google-coral/edgetpu/issues/27 0x0000000000431e18 in _Py_UnixMain ()\r\nhttps://github.com/google-coral/edgetpu/issues/28 0x0000007ff7ca6d24 in __libc_start_main () from /lib/aarch64-linux-gnu/libc.so.6\r\nhttps://github.com/google-coral/edgetpu/issues/29 0x000000000042c3b0 in _start ()\r\n\r\nHi, I got this error message by using gdb, it seems like something wrong with the interpreter but not sure what is the exact problem", "Hi, @pjpratik \r\n\r\nCould you please look into this issue. Thank you!", "Hi, I have resolved this issue by changing the package I'm using when inferencing. Thanks.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59604\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59604\">No</a>\n", "> Hi, I have resolved this issue by changing the package I'm using when inferencing. Thanks.\r\n\r\nCan you tell me what package?\r\n" ]

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[ "Hi @joesho112358 Can you please resolve conflicts? Thank you!", "This PR is stale because it has been open for 14 days with no activity. It will be closed if no further activity occurs. Thank you.", "This PR was closed because it has been inactive for 14 days since being marked as stale. Please reopen if you'd like to work on this further." ]

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[ "Hi @vysarge Can you please resolve conflicts? Thank you!", "This PR is stale because it has been open for 14 days with no activity. It will be closed if no further activity occurs. Thank you.", "Conflicts have been resolved.", "Hi @JXRiver Can you please review this PR ? Thank you!", "Hi @JXRiver Can you please review this PR ? Thank you!", "Hi @JXRiver Can you please review this PR ? Thank you!", "@JXRiver you self assigned here. Can you PTAL or reassign to someone with better context. ", "Closed the prerequisite of this PR github.com/tensorflow/tensorflow/pull/59508. Going to close this one as well." ]

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[ "@linuxreitt Could you please make sure to check the tested build configuration as mentioned [here](https://www.tensorflow.org/install/source#tested_build_configurations) and use the latest TF version.\r\nThank you! ", "This issue has been automatically marked as stale because it has no recent activity. It will be closed if no further activity occurs. Thank you.\n", "Closing as stale. Please reopen if you'd like to work on this further.\n", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59601\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59601\">No</a>\n" ]

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[ "Hi, You can find the source code for all the implementation of MLIR on TFLite here https://github.com/tensorflow/tensorflow/tree/master/tensorflow/compiler/mlir/lite/ir.\r\nThe source code is mainly implemented in `C/C++` and `.td `", "This issue has been automatically marked as stale because it has no recent activity. It will be closed if no further activity occurs. Thank you.\n", "Closing as stale. Please reopen if you'd like to work on this further. Thanks!", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59600\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59600\">No</a>\n" ]

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[ "@penpornk I have addressed your review comments. Can you please take a look?", "Hi @penpornk Can you please review this PR ? Thank you!", "@penpornk Gentle reminder for PR review. Please let me know if you have any questions. Thanks.", "@penpornk Thanks for the PR review. I have addressed all your review comments. Can you please take a look?", "Closing the PR since it has been merged in https://github.com/tensorflow/tensorflow/commit/2802c538daff0ad369c8c8243f1f0f9b605f51d1." ]

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[ "@dburian Thanks for reporting the issue.\r\n\r\nI have tried in google colab and the auto complete suggestions are working fine. Please refer the below screenshot.\r\n\r\n\r\n\r\nI have tried with other modules as well and observed the same behaviour. Thanks!\r\n\r\n", "That's great, yet I experience the lack of auto-complete with **pyright**, not **colab**. I posted the issue to see the possible solutions (either from others or the developers). \r\n", "@dburian Thanks for the clarification.\r\n\r\n@sushreebarsa Could you please look into this. Thank you.", "Hi, Thanks for opening the issue, we are aware of this issue in some of the IDE's which users have reported. \r\nWe are working on the issue, for any progress regarding the similar issue, refer the threads in the issues here https://github.com/tensorflow/tensorflow/issues/56231, https://github.com/tensorflow/tensorflow/issues/36798", "@dburian @sachinprasadhs I think the Issue here will probably solved with https://github.com/microsoft/pylance-release/issues/3937? Which has the start of https://github.com/python/typeshed/issues/7144 as an underlying reason.\r\n\r\nI mean tensorflow has other autocompletion problems, but at least the apearance of nothing working with pyright version 1.1.292 is probably caused by that.", "Thanks a lot @alkatar21. Indeed in my case, the lack of auto-completion was caused by the (now incomplete) type stubs shipped with pyright, which pyright prefers over code inspection. For anyone experiencing the same issues try following the issues linked in alkatar's comment if you like to know the whys and whats.\r\n\r\nTLDR; Try deleting 'tensorflow' directory under 'dist/typeshed-fallback/stubs'.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59597\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59597\">No</a>\n" ]

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[ "Hi, @SuryanarayanaY \r\n\r\nCould you please look into this issue? Thank you!", "Hi @RoboTux ,\r\n\r\nCould you please confirm the sequence of commands you used for build.\r\nAlso please provide the error logs that may help us to analyse the issue. Thanks!", "1. Delete bazel cache\r\n2. Run `bazel build --config=dbg -c opt tensorflow/compiler/mlir:tf-opt`\r\n3. ls bazel-out/*-opt/bin/external/llvm-project/llvm/lib/Target\r\n\r\nWhich for me shows AArch64, AMDGPU, ARM and X86. I have a WIP patch that allow me to select the targets I want enabled and have for instance only AArch64.", "This issue has been automatically marked as stale because it has no recent activity. It will be closed if no further activity occurs. Thank you.\n", "@RoboTux ,\r\n\r\nThanks for confirmation.If this can be doable then definitely it will save some time AFAIK. Do you have plan to raise a PR to enable this feature? Our team may review and if all well may approve. Thanks!", "> @RoboTux ,\r\n> \r\n> Thanks for confirmation.If this can be doable then definitely it will save some time AFAIK. Do you have plan to raise a PR to enable this feature? Our team may review and if all well may approve. Thanks!\r\n\r\nAbsolutely, I was waiting on someone to confirm it's a desirable feature. I'll be running some tests and publish it shortly.", "@RoboTux ,\r\n\r\nI have tried to build from source on Ubuntu 20 VM and I got the Targets as `AMDGPU X86`. Please refer the attached log.\r\n\r\n```\r\n(bazel) suryanarayanay@surya-ubuntu20:~/tensorflow$ bazel build --config=dbg -c opt tensorflow/compiler/mlir:tf-opt\r\nStarting local Bazel server and connecting to it...\r\nINFO: Options provided by the client:\r\n Inherited 'common' options: --isatty=1 --terminal_columns=100\r\nINFO: Reading rc options for 'build' from /home/suryanarayanay/tensorflow/.bazelrc:\r\n Inherited 'common' options: --experimental_repo_remote_exec\r\nINFO: Reading rc options for 'build' from /home/suryanarayanay/tensorflow/.bazelrc:\r\n 'build' options: --define framework_shared_object=true --define tsl_protobuf_header_only=true --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_library --experimental_link_static_libraries_once=false --incompatible_enforce_config_setting_visibility --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/ir,tensorflow/compiler/mlir/tfrt/tests/analysis,tensorflow/compiler/mlir/tfrt/tests/jit,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_tfrt,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_jitrt,tensorflow/compiler/mlir/tfrt/tests/tf_to_corert,tensorflow/compiler/mlir/tfrt/tests/tf_to_tfrt_data,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/tfrt/eager,tensorflow/core/tfrt/eager/backends/cpu,tensorflow/core/tfrt/eager/backends/gpu,tensorflow/core/tfrt/eager/core_runtime,tensorflow/core/tfrt/eager/cpp_tests/core_runtime,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils\r\nINFO: Found applicable config definition build:short_logs in file /home/suryanarayanay/tensorflow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING\r\nINFO: Found applicable config definition build:v2 in file /home/suryanarayanay/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1\r\nINFO: Found applicable config definition build:dbg in file /home/suryanarayanay/tensorflow/.bazelrc: -c dbg --per_file_copt=+.*,-tensorflow.*@-g0 --per_file_copt=+tensorflow/core/kernels.*@-g0 --cxxopt -DTF_LITE_DISABLE_X86_NEON --copt -DDEBUG_BUILD\r\nINFO: Found applicable config definition build:linux in file /home/suryanarayanay/tensorflow/.bazelrc: --host_copt=-w --copt=-Wno-all --copt=-Wno-extra --copt=-Wno-deprecated --copt=-Wno-deprecated-declarations --copt=-Wno-ignored-attributes --copt=-Wno-array-bounds --copt=-Wunused-result --copt=-Werror=unused-result --copt=-Wswitch --copt=-Werror=switch --copt=-Wno-error=unused-but-set-variable --define=PREFIX=/usr --define=LIBDIR=$(PREFIX)/lib --define=INCLUDEDIR=$(PREFIX)/include --define=PROTOBUF_INCLUDE_PATH=$(PREFIX)/include --cxxopt=-std=c++17 --host_cxxopt=-std=c++17 --config=dynamic_kernels --distinct_host_configuration=false --experimental_guard_against_concurrent_changes\r\nINFO: Found applicable config definition build:dynamic_kernels in file /home/suryanarayanay/tensorflow/.bazelrc: --define=dynamic_loaded_kernels=true --copt=-DAUTOLOAD_DYNAMIC_KERNELS\r\nINFO: Analyzed target //tensorflow/compiler/mlir:tf-opt (372 packages loaded, 24423 targets configured).\r\nINFO: Found 1 target...\r\nTarget //tensorflow/compiler/mlir:tf-opt up-to-date:\r\n bazel-bin/tensorflow/compiler/mlir/tf-opt\r\nINFO: Elapsed time: 4232.262s, Critical Path: 377.67s\r\nINFO: 11031 processes: 1009 internal, 10022 local.\r\nINFO: Build completed successfully, 11031 total actions\r\n(bazel) suryanarayanay@surya-ubuntu20:~/tensorflow$ ls bazel-out/*-opt/bin/external/llvm-project/llvm/lib/Target\r\nAMDGPU X86\r\n```\r\nI am not getting AArch64 ARM for targets. Could you please cross check and confirm ? Please note I have tested on Ubuntu 20 machine.\r\n\r\nThanks!\r\n", "> @RoboTux ,\r\n> \r\n> I have tried to build from source on Ubuntu 20 VM and I got the Targets as `AMDGPU X86`. Please refer the attached log.\r\n> \r\n> ```\r\n> (bazel) suryanarayanay@surya-ubuntu20:~/tensorflow$ bazel build --config=dbg -c opt tensorflow/compiler/mlir:tf-opt\r\n> Starting local Bazel server and connecting to it...\r\n> INFO: Options provided by the client:\r\n> Inherited 'common' options: --isatty=1 --terminal_columns=100\r\n> INFO: Reading rc options for 'build' from /home/suryanarayanay/tensorflow/.bazelrc:\r\n> Inherited 'common' options: --experimental_repo_remote_exec\r\n> INFO: Reading rc options for 'build' from /home/suryanarayanay/tensorflow/.bazelrc:\r\n> 'build' options: --define framework_shared_object=true --define tsl_protobuf_header_only=true --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_library --experimental_link_static_libraries_once=false --incompatible_enforce_config_setting_visibility --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/ir,tensorflow/compiler/mlir/tfrt/tests/analysis,tensorflow/compiler/mlir/tfrt/tests/jit,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_tfrt,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_jitrt,tensorflow/compiler/mlir/tfrt/tests/tf_to_corert,tensorflow/compiler/mlir/tfrt/tests/tf_to_tfrt_data,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/tfrt/eager,tensorflow/core/tfrt/eager/backends/cpu,tensorflow/core/tfrt/eager/backends/gpu,tensorflow/core/tfrt/eager/core_runtime,tensorflow/core/tfrt/eager/cpp_tests/core_runtime,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils\r\n> INFO: Found applicable config definition build:short_logs in file /home/suryanarayanay/tensorflow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING\r\n> INFO: Found applicable config definition build:v2 in file /home/suryanarayanay/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1\r\n> INFO: Found applicable config definition build:dbg in file /home/suryanarayanay/tensorflow/.bazelrc: -c dbg --per_file_copt=+.*,-tensorflow.*@-g0 --per_file_copt=+tensorflow/core/kernels.*@-g0 --cxxopt -DTF_LITE_DISABLE_X86_NEON --copt -DDEBUG_BUILD\r\n> INFO: Found applicable config definition build:linux in file /home/suryanarayanay/tensorflow/.bazelrc: --host_copt=-w --copt=-Wno-all --copt=-Wno-extra --copt=-Wno-deprecated --copt=-Wno-deprecated-declarations --copt=-Wno-ignored-attributes --copt=-Wno-array-bounds --copt=-Wunused-result --copt=-Werror=unused-result --copt=-Wswitch --copt=-Werror=switch --copt=-Wno-error=unused-but-set-variable --define=PREFIX=/usr --define=LIBDIR=$(PREFIX)/lib --define=INCLUDEDIR=$(PREFIX)/include --define=PROTOBUF_INCLUDE_PATH=$(PREFIX)/include --cxxopt=-std=c++17 --host_cxxopt=-std=c++17 --config=dynamic_kernels --distinct_host_configuration=false --experimental_guard_against_concurrent_changes\r\n> INFO: Found applicable config definition build:dynamic_kernels in file /home/suryanarayanay/tensorflow/.bazelrc: --define=dynamic_loaded_kernels=true --copt=-DAUTOLOAD_DYNAMIC_KERNELS\r\n> INFO: Analyzed target //tensorflow/compiler/mlir:tf-opt (372 packages loaded, 24423 targets configured).\r\n> INFO: Found 1 target...\r\n> Target //tensorflow/compiler/mlir:tf-opt up-to-date:\r\n> bazel-bin/tensorflow/compiler/mlir/tf-opt\r\n> INFO: Elapsed time: 4232.262s, Critical Path: 377.67s\r\n> INFO: 11031 processes: 1009 internal, 10022 local.\r\n> INFO: Build completed successfully, 11031 total actions\r\n> (bazel) suryanarayanay@surya-ubuntu20:~/tensorflow$ ls bazel-out/*-opt/bin/external/llvm-project/llvm/lib/Target\r\n> AMDGPU X86\r\n> ```\r\n> \r\n> I am not getting AArch64 ARM for targets. Could you please cross check and confirm ? Please note I have tested on Ubuntu 20 machine.\r\n> \r\n> Thanks!\r\n\r\nI can confirm when building on x86 Ubuntu 22.04. I see that utils/bazel/llvm-project-overlay/llvm/BUILD.bazel has some config_setting that check the cpu value which might explain why that is. But then it's strange to have x86 when doing an AArch64 build. I'll investigate further but that most likely means reworking the patch since with it by default it builds all CPU targets.", "Hi @RoboTux ,\r\n\r\nI would like to know whether did you changes any code in .bazelrc or any other bazel files etc which might configured Target for different platforms.I have not changed any files and got build for only one Target. Please provide all the changes you have done(if any) to enable the build for multiple targets.\r\n\r\nThank you!", "Hi @SuryanarayanaY ,\r\n\r\nSorry for the slow reply. I've found why X86 when building on AArch64 but not other LLVM targets thanks to the great bazel query allpaths feature:\r\n\r\n@llvm-project/llvm:X86CodeGen gets pulled from //tensorflow/compiler/xla/service/cpu:cpu_compiler, as well as lower down from //tensorflow/compile/xla/runtime:execution_engine and from //tensorflow/compiler/xla/mlir/runtime/transforms:jit_compiler.\r\n\r\nOther targets dependency are guarded by select around targets in //tensorflow/tsl (e.g.arm_any or linux_ppc64le) or by if_llvm_<target>_available. Now the patch contributed in PR59995 does solve that but enables all CPU targets by default because I erroneously thought it was the default. I'll rework it to only enable the host CPU target by default.\r\n\r\n> \r\n> I would like to know whether did you changes any code in .bazelrc or any other bazel files etc which might configured Target for different platforms.I have not changed any files and got build for only one Target. Please provide all the changes you have done(if any) to enable the build for multiple targets.\r\n> \r\n\r\nTo answer your question I have no .bazelrc except for the one in tensorflow and my .tf_configure.bazelrc is what comes by default when running configure:\r\n```\r\n\r\nbuild --action_env PYTHON_BIN_PATH=\"/usr/bin/python3.10\"\r\nbuild --action_env PYTHON_LIB_PATH=\"/usr/lib/python3/dist-packages\"\r\nbuild --python_path=\"/usr/bin/python3.10\"\r\nbuild:opt --copt=-Wno-sign-compare\r\nbuild:opt --host_copt=-Wno-sign-compare\r\ntest --flaky_test_attempts=3\r\ntest --test_size_filters=small,medium\r\ntest:v1 --test_tag_filters=-benchmark-test,-no_oss,-oss_excluded,-gpu,-oss_serial\r\ntest:v1 --build_tag_filters=-benchmark-test,-no_oss,-oss_excluded,-gpu\r\ntest:v2 --test_tag_filters=-benchmark-test,-no_oss,-oss_excluded,-gpu,-oss_serial,-v1only\r\ntest:v2 --build_tag_filters=-benchmark-test,-no_oss,-oss_excluded,-gpu,-v1only\r\n```\r\n\r\nAs mentioned, this is on AArch64 host which is what makes the difference.", "@RoboTux ,\r\n\r\nThe build I was done was on a machine with `X86` architecture which seems building the target for that particular architecture only. Whereas as per your investigation for `AArch64` it is building for both `AArch64` as well as `X86` which might be the reason for getting different Targets for me.\r\n\r\nPlease continue working on the Patch and I hope this will be approved by dev-team.\r\n\r\nThanks!\r\n\r\n\r\n\r\n\r\n\r\n", "Patch has been merged.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59595\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59595\">No</a>\n" ]

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[ "before above error, it happened\r\nERROR: --experimental_link_static_libraries_once=false :: Unrecognized option: --experimental_link_static_libraries_once=false\r\n.so I delete experimental_link_static_libraries_once option", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59592\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59592\">No</a>\n" ]

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[ "Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).\n\nView this [failed invocation](https://github.com/tensorflow/tensorflow/pull/59591/checks?check_run_id=11159740231) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request.", "This is #58285, why not use that one?" ]

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[ "@dhruvsreenivas Thanks for reporting issue.\r\n\r\nPosting relevant issue threads [#58606](https://github.com/tensorflow/tensorflow/issues/58606), [#57690](https://github.com/tensorflow/tensorflow/issues/57690), [56624](https://github.com/tensorflow/tensorflow/issues/56624).\r\n\r\n@sushreebarsa Could you please look into this. Thank you.", "@pjpratik thank you! I'll check these out.\r\n\r\nIf you guys need any help with error reproduction in Colab, let me know and I'll be glad to help.", "Alright, so as far as I can see, there is no definite solution posted -- I think some issues haven't necessarily been resolved yet, but they are shown to be legitimate (i.e. reproduced). What is the best course of action here? I'd like to know what to do as soon as possible. Thank you so much!", "@dhruvsreenivas Sorry for the late response!\r\nCould you please share a simple standalone code to replicate this issue?\r\nThank you!", "Alright so the codebase is a bit big -- there are multiple files that generally contribute to the issue (i.e. parts from all these files go toward running the training loop, which will end in an OOM error), all generally organized in this one github [repo](https://github.com/dhruvsreenivas/byol-offline). I can give instructions on how to install everything and run the repo code if you want, but that might not be the best use of time.\r\n\r\nI'm a bit busy now with some other work, so give me some time--I'll see if I can make a colab notebook showing everything over the weekend, and get back to you. How does that sound?", "Hi, @dhruvsreenivas \r\n\r\nApologize for the delay and as you mentioned in your previous comment, you'll share one Google Colab notebook to show us TF dataset generator memory leak issue so May I know have you prepared that Google Colab notebook now ? If yes please share that notebook with us, we'll try to replicate the same issue from our end. Thank you!", "Hey everyone,\r\n\r\nSorry I've been super busy with some school work over the past few weeks -- I'll try to make the Colab by early next week, and will send upon completion. Thanks so much!\r\n\r\nBest,\r\nDhruv Sreenivas", "Alright, I think it might not be a TensorFlow problem as much as it could just be I'm loading in a LOT of data at once, so memory might be at a premium -- I've tried this with a couple other experiments and it seems that this is what is going on. Sorry for the inconvenience!\r\n\r\nFor example, I tried something simple where I would delete the batch once I finished updating with it, and that actually worked well with limited memory.", "Hi, @dhruvsreenivas \r\n\r\nNo problem at all, Good to hear that, You're able to resolve your issue, Thank you for sharing the detailed information about root cause for your issue and If you're having training data simply cannot fit in memory then I would suggest you to have look at this article [tf.data.Dataset generators with parallelization](https://medium.com/@acordier/tf-data-dataset-generators-with-parallelization-the-easy-way-b5c5f7d2a18) which may help you to solve your memory related issue.\r\n\r\nIf you need further assistance please let us know ? or Could you please confirm if this issue is resolved for you ? Please feel free to close the issue if it is resolved ? Thank you!", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59587\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59587\">No</a>\n" ]

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[ "Hi, @trickiwoo \r\n\r\nI was able to replicate the issue on `Ubuntu 20.04` with `tf-nightly-2.13.0-dev20230204` with GPU and even I also replicated on Google Colab(`GPU`) with `tf-nightly-2.13.0-dev20230207` here is [gist file](https://colab.research.google.com/gist/gaikwadrahul8/548c2266dc8e4f53b60bd8617830e9f2/-59583-tf-nightly-gpu.ipynb) for your reference. I was getting the same error message which you mentioned in the above error log. I have added error screenshot below for your reference so it seems like we will have to dig more into this issue with `GPU` execution to find out root cause for it so we will update soon.\r\n\r\nI replicated the same issue with `CPU` on `Ubuntu 20.04` with `tf-nightly-2.13.0-dev20230204` and on Google Colab, here is [gist file ](https://colab.research.google.com/gist/gaikwadrahul8/733b3c800824e3edfdd7546ff5556727/-59583-tf-nightly-cpu.ipynb)and it's not throwing error message `tensorflow.python.framework.errors_impl.InternalError: Assigned device '/job:localhost/replica:0/task:0/device:GPU:0' does not have registered OpKernel support for _Arg [[{{node input}}]] [Op:Bitcast] `\r\n\r\n\r\nHere is screenshot of CPU execution :\r\n\r\n\r\n\r\nHere is screenshot of GPU execution :\r\n\r\n\r\n\r\nThank you for noticing the issue and we will update you soon. Thank you!", "Internal error is a valid error message if arguments are invalid.\r\n\r\nIn this case, you are trying to run a kernel on a device that does not support it with the given types", "This issue has been automatically marked as stale because it has no recent activity. It will be closed if no further activity occurs. Thank you.\n", "Closing as stale. Please reopen if you'd like to work on this further.\n", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59583\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59583\">No</a>\n" ]

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[ "@trickiwoo \r\nI was able to reproduce the issue on Colab using tf-nightly but I was able to execute the given code on Colab using TF v2.11. tf-nightly which is unstable, Could you please try on the latest stable version which is TF v2.11 and find the gist of [TF v2.11](https://colab.research.google.com/gist/tiruk007/0c1f6cef9de20d9ad88e1d367da00c6d/59582.ipynb) and [tf-nightly](https://colab.research.google.com/gist/tiruk007/a55d40d9a3a0232ab6426ad1de08113e/59582_nightly.ipynb) for reference . Let us know if it helps.\r\n\r\nThank you!", "This issue has been automatically marked as stale because it has no recent activity. It will be closed if no further activity occurs. Thank you.\n", "Closing as stale. Please reopen if you'd like to work on this further.\n", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59582\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59582\">No</a>\n" ]

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[ "@penpornk Hi, I notice that there is a failure with \"feedback/copybara\" check but I could not access the \"Details\". \r\n\r\nPlease let me know if there is anything I need to do. Thanks!", "Hi @gzmkl,\r\n\r\nIt was failing internal Windows CPU presubmit (not sure why the github one didn't fail..)\r\nThe test that failed is `//tensorflow/core/kernels/mkl:mkl_quantized_conv_ops_test`\r\n```\r\n[ FAILED ] 11 tests, listed below:\r\n[ FAILED ] QuantizedConvTest.BiasAddFusion\r\n[ FAILED ] QuantizedConvTest.BiasAddRequantizeFusion\r\n[ FAILED ] QuantizedConvTest.BiasAddReluRequantizeFusion\r\n[ FAILED ] QuantizedConvTest.UnsignedInputBiasAddReluRequantizeFusion\r\n[ FAILED ] QuantizedConvTest.DWBiasAddFusion\r\n[ FAILED ] QuantizedConvTest.DWBiasAddRequantizeFusion\r\n[ FAILED ] QuantizedConvTest.DWBiasAddReluRequantizeFusion\r\n[ FAILED ] QuantizedConvTest.DWUnsignedInputBiasAddReluRequantizeFusion\r\n[ FAILED ] QuantizedConvTest.BiasAddSumReluRequantizeFusion\r\n[ FAILED ] QuantizedConvTest.BiasAddSumReluRequantizeFusionSignedSummand\r\n[ FAILED ] QuantizedConvTest.BiasAddSumReluFusionFloatSummand\r\n```\r\nTest command:\r\n```\r\n<path_to_bazel.exe> test //tensorflow/core/kernels/mkl:mkl_quantized_conv_ops_test --test_output=all\r\n```\r\n\r\nExample test failure log:\r\n```\r\n[ RUN ] QuantizedConvTest.BiasAddFusion\r\ntensorflow/core/framework/tensor_testutil.cc(184): error: Value of: IsClose(Tx[i], Ty[i], typed_atol, typed_rtol)\r\n Actual: false (-25 not close to -39.473743438720703)\r\nExpected: true\r\ni = 0 Tx[i] = -25 Ty[i] = -39.473743438720703\r\ntensorflow/core/framework/tensor_testutil.cc(184): error: Value of: IsClose(Tx[i], Ty[i], typed_atol, typed_rtol)\r\n Actual: false (-5 not close to 5.9996280670166016)\r\nExpected: true\r\ni = 1 Tx[i] = -5 Ty[i] = 5.9996280670166016\r\ntensorflow/core/framework/tensor_testutil.cc(184): error: Value of: IsClose(Tx[i], Ty[i], typed_atol, typed_rtol)\r\n Actual: false (19 not close to 17.189409255981445)\r\nExpected: true\r\ni = 2 Tx[i] = 19 Ty[i] = 17.189409255981445\r\ntensorflow/core/framework/tensor_testutil.cc(184): error: Value of: IsClose(Tx[i], Ty[i], typed_atol, typed_rtol)\r\n Actual: false (14 not close to 30.283836364746094)\r\nExpected: true\r\ni = 3 Tx[i] = 14 Ty[i] = 30.283836364746094\r\ntensorflow/core/framework/tensor_testutil.cc(184): error: Value of: IsClose(Tx[i], Ty[i], typed_atol, typed_rtol)\r\n Actual: false (10 not close to -7.0947980880737305)\r\nExpected: true\r\ni = 4 Tx[i] = 10 Ty[i] = -7.0947980880737305\r\ntensorflow/core/framework/tensor_testutil.cc(184): error: Value of: IsClose(Tx[i], Ty[i], typed_atol, typed_rtol)\r\n Actual: false (25 not close to 22.093868255615234)\r\nExpected: true\r\ni = 5 Tx[i] = 25 Ty[i] = 22.093868255615234\r\ntensorflow/core/framework/tensor_testutil.cc(184): error: Value of: IsClose(Tx[i], Ty[i], typed_atol, typed_rtol)\r\n Actual: false (-8 not close to -10.99931812286377)\r\nExpected: true\r\ni = 6 Tx[i] = -8 Ty[i] = -10.99931812286377\r\ntensorflow/core/framework/tensor_testutil.cc(184): error: Value of: IsClose(Tx[i], Ty[i], typed_atol, typed_rtol)\r\n Actual: false (0 not close to 1.9046437740325928)\r\nExpected: true\r\ni = 7 Tx[i] = 0 Ty[i] = 1.9046437740325928\r\ntensorflow/core/framework/tensor_testutil.cc(184): error: Value of: IsClose(Tx[i], Ty[i], typed_atol, typed_rtol)\r\n Actual: false (12 not close to 34.1883544921875)\r\nExpected: true\r\ni = 9 Tx[i] = 12 Ty[i] = 34.1883544921875\r\ntensorflow/core/framework/tensor_testutil.cc(184): error: Value of: IsClose(Tx[i], Ty[i], typed_atol, typed_rtol)\r\n Actual: false (-15 not close to -23.284269332885742)\r\nExpected: true\r\ni = 10 Tx[i] = -15 Ty[i] = -23.284269332885742\r\ntensorflow/core/framework/tensor_testutil.cc(187): error: Expected: (num_failures) < (max_failures), actual: 10 vs 10\r\n```\r\n\r\nI'm trying to get to this by tomorrow, but it would be great if you could help debug on your side too (if you could reproduce it). Thank you! ", "Hi @gzmkl,\r\n\r\nI realized that Github's [Windows Bazel presubmit](https://source.cloud.google.com/results/invocations/26083756-8c82-48e9-9d00-6ec3c1e178a8/log) on this PR didn't run any `mkl_` tests at all. So it's not surprising that it passed.\r\n\r\nWere you able to reproduce the test failure on your system? If it's helpful, the CPU we used has up to AVX2.\r\n\r\nSince it doesn't seem like some simple merging issue, but could be actual issues with the PR, I'll leave the debugging to you. If we can merge this PR by this Wednesday, I can try to cherry-pick this into TF 2.12. Otherwise, we can wait for the patch release (v2.12.1) once this PR is merged. Thank you!\r\n ", "@penpornk Thank you for the inputs. Yes, some INT8 (mkl_*quant*test*) does not run on Windows. TF Windows team (Mayank) and Ramesh also gave me the same feedback. \r\n \r\nI just made code change by adding back \" &&defined(ENABLE_MKL)\" guard in related tests and pushed the commit.\r\nThe code change has passed internal Windows UT tests. \r\n", "Exactly. This has been included in our TODO list as we want to enable some of the tests on Windows along with stock TF. \r\n\r\n" ]

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[ "Hi @aurelliafirsty,\r\nIn order to expedite the trouble-shooting process, we request you to share the colab link to replicate the issue reported here. Thank you!\t\r\n", "https://colab.research.google.com/drive/14Kczx0SrM_t3zvLagbqtesnfF0gieEtZ?authuser=2", "I'm facing a different error while replicating the above code in TF v2.9. Please find the gist [here](https://colab.sandbox.google.com/gist/synandi/1866e1f3216d56981e4fc1964bcfa597/efficientdet.ipynb). Thank you!", "This issue has been automatically marked as stale because it has no recent activity. It will be closed if no further activity occurs. Thank you.\n", "Closing as stale. Please reopen if you'd like to work on this further.\n", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59578\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59578\">No</a>\n", "I encountered the same issue a few weeks ago. It was solved by reducing the batch size. I had to go from a batch size of 16 to 4 with 10gb of vram.", "> I encountered the same issue a few weeks ago. It was solved by reducing the batch size. I had to go from a batch size of 16 to 4 with 10gb of vram.\r\n\r\nI am facing the same error, but I could not solve it by reducing the batch size, even to 1. The curious thing is that this error occurs only on Linux, not on Windows. ", "Facing the same error on Colab, strangely enough with a lower batch size the error doesn't actually stop the training, so I'm managing with a batch size of 4 (8 takes up more the 16GB of VRAM in my case)", "I have the same issue here\r\n\r\ntensorflow/core/grappler/optimizers/meta_optimizer.cc:954] layout failed: INVALID_ARGUMENT: Size of values 0 does not match size of permutation 4 @ fanin shape inmodel_5/dropout_10/dropout/SelectV2-2-TransposeNHWCToNCHW-LayoutOptimizer\r\n\r\nthis is my code\r\n\r\n```\r\nimport tensorflow as tf\r\nfrom tensorflow.keras.models import Model\r\nfrom tensorflow.keras.layers import *\r\n\r\n# Set the input shape of the images (adjust based on the input image size)\r\ninput_shape = (128, 128, 3) # Adjust based on the input image size\r\n\r\n# Set the number of segmentation classes \r\nn_classes = 1 # Number of segmentation classes\r\n\r\n# Define the model architecture\r\ninputs = Input(shape=input_shape) # Define the input layer with the specified input shape\r\n\r\n# Encoder\r\nconv1 = Conv2D(64, (3, 3), activation='relu', padding='same')(inputs) # First convolutional layer with 64 filters\r\nconv1 = BatchNormalization()(conv1) # Apply batch normalization to normalize the activations\r\nconv1 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv1) # Second convolutional layer with 64 filters\r\nconv1 = BatchNormalization()(conv1) # Apply batch normalization to normalize the activations\r\npool1 = MaxPooling2D((2, 2))(conv1) # Max pooling layer with a pool size of (2, 2)\r\n\r\nconv2 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool1) # Convolutional layer with 128 filters\r\nconv2 = BatchNormalization()(conv2) # Apply batch normalization to normalize the activations\r\nconv2 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv2) # Convolutional layer with 128 filters\r\nconv2 = BatchNormalization()(conv2) # Apply batch normalization to normalize the activations\r\npool2 = MaxPooling2D((2, 2))(conv2) # Max pooling layer with a pool size of (2, 2)\r\n\r\nconv3 = Conv2D(256, (3, 3), activation='relu', padding='same')(pool2) # Convolutional layer with 256 filters\r\nconv3 = BatchNormalization()(conv3) # Apply batch normalization to normalize the activations\r\nconv3 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv3) # Convolutional layer with 256 filters\r\nconv3 = BatchNormalization()(conv3) # Apply batch normalization to normalize the activations\r\npool3 = MaxPooling2D((2, 2))(conv3) # Max pooling layer with a pool size of (2, 2)\r\n\r\nconv4 = Conv2D(512, (3, 3), activation='relu', padding='same')(pool3) # Convolutional layer with 512 filters\r\nconv4 = BatchNormalization()(conv4) # Apply batch normalization to normalize the activations\r\nconv4 = Conv2D(512, (3, 3), activation='relu', padding='same')(conv4) # Convolutional layer with 512 filters\r\nconv4 = BatchNormalization()(conv4) # Apply batch normalization to normalize the activations\r\ndrop4 = Dropout(0.5)(conv4) # Apply dropout regularization with a rate of 0.5\r\npool4 = MaxPooling2D((2, 2))(drop4) # Max pooling layer with a pool size of (2, 2)\r\n\r\nconv5 = Conv2D(1024, (3, 3), activation='relu', padding='same')(pool4) # Convolutional layer with 1024 filters\r\nconv5 = BatchNormalization()(conv5) # Apply batch normalization to normalize the activations\r\nconv5 = Conv2D(1024, (3, 3), activation='relu', padding='same')(conv5) # Convolutional layer with 1024 filters\r\nconv5 = BatchNormalization()(conv5) # Apply batch normalization to normalize the activations\r\ndrop5 = Dropout(0.5)(conv5) # Apply dropout regularization with a rate of 0.5\r\n\r\n# Decoder\r\nup6 = concatenate([UpSampling2D((2, 2))(drop5), conv4], axis=-1) # Upsampling layer with a scale factor of (2, 2)\r\nconv6 = Conv2D(512, (3, 3), activation='relu', padding='same')(up6) # Convolutional layer with 512 filters\r\nconv6 = BatchNormalization()(conv6) # Apply batch normalization to normalize the activations\r\nconv6 = Conv2D(512, (3, 3), activation='relu', padding='same')(conv6) # Convolutional layer with 512 filters\r\nconv6 = BatchNormalization()(conv6) # Apply batch normalization to normalize the activations\r\n\r\nup7 = concatenate([UpSampling2D((2, 2))(conv6), conv3], axis=-1) # Upsampling layer with a scale factor of (2, 2)\r\nconv7 = Conv2D(256, (3, 3), activation='relu', padding='same')(up7) # Convolutional layer with 256 filters\r\nconv7 = BatchNormalization()(conv7) # Apply batch normalization to normalize the activations\r\nconv7 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv7) # Convolutional layer with 256 filters\r\nconv7 = BatchNormalization()(conv7) # Apply batch normalization to normalize the activations\r\n\r\nup8 = concatenate([UpSampling2D((2, 2))(conv7), conv2], axis=-1) # Upsampling layer with a scale factor of (2, 2)\r\nconv8 = Conv2D(128, (3, 3), activation='relu', padding='same')(up8) # Convolutional layer with 128 filters\r\nconv8 = BatchNormalization()(conv8) # Apply batch normalization to normalize the activations\r\nconv8 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv8) # Convolutional layer with 128 filters\r\nconv8 = BatchNormalization()(conv8) # Apply batch normalization to normalize the activations\r\n\r\nup9 = concatenate([UpSampling2D((2, 2))(conv8), conv1], axis=-1) # Upsampling layer with a scale factor of (2, 2)\r\nconv9 = Conv2D(64, (3, 3), activation='relu', padding='same')(up9) # Convolutional layer with 64 filters\r\nconv9 = BatchNormalization()(conv9) # Apply batch normalization to normalize the activations\r\nconv9 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv9) # Convolutional layer with 64 filters\r\nconv9 = BatchNormalization()(conv9) # Apply batch normalization to normalize the activations\r\n\r\noutputs = Conv2D(n_classes, (1, 1), activation='softmax')(conv9) # Convolutional layer for output\r\n\r\n# Create the model\r\nmodel = Model(inputs=inputs, outputs=outputs)\r\n\r\n# Print the model summary\r\nmodel.summary()\r\n\r\n\r\n# Set the optimizer for the model\r\noptimizer = tf.keras.optimizers.Adam(lr=1e-4)\r\n\r\n# Compile the model with loss function and metrics\r\nmodel.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy'])\r\n\r\nimages_path = '/kaggle/input/coco-2014-dataset-for-yolov3/coco2014/images/val2014'\r\nmasks_path = '/kaggle/working/mask_val_2014'\r\nbatch_size = 8\r\n\r\nval_generator = CustomDataGenerator(images_path, masks_path, batch_size)\r\n\r\n\r\n# Fit the model with the training generator\r\ntrain_steps = len(os.listdir( \"/kaggle/working/mask_train_2014/\"))/batch_size\r\nmodel.fit(train_generator,validation_data = val_generator, steps_per_epoch = train_steps , epochs=20)\r\n```\r\n\r\nAlso to ensure that all the input and output have proper shape I run the code below\r\n\r\n```\r\ndef print_preprocessed_image_shapes(model, generator):\r\n \"\"\"\r\n Print the shapes of preprocessed images generated by the provided model and generator.\r\n\r\n Args:\r\n model (tf.keras.Model): The trained model.\r\n generator (CustomDataGenerator): Instance of the CustomDataGenerator class.\r\n \"\"\"\r\n for i in range(len(generator)):\r\n # Get a batch of preprocessed images from the generator\r\n batch_images, batch_masks = generator[i]\r\n\r\n # Print the shapes of the preprocessed images\r\n for image in batch_images:\r\n print(f\"Shape of preprocessed image: {image.shape}\")\r\n for mask in batch_maskss:\r\n\r\n print(f\"Shape of preprocessed image: {mask.shape}\")\r\n \r\n# Print the shapes of preprocessed images\r\nprint_preprocessed_image_shapes(model, val_generator)\r\n```\r\n\r\n\r\nAs a result of this error, the model was unable to undergo the training process.\r\n\r\n\r\n\r\n\r\n\r\n", "Related to https://github.com/tensorflow/tensorflow/issues/61324 and https://github.com/tensorflow/tensorflow/issues/34499", "> Facing the same error on Colab, strangely enough with a lower batch size the error doesn't actually stop the training, so I'm managing with a batch size of 4 (8 takes up more the 16GB of VRAM in my case)\r\n\r\nFor me it's the same. The training was done successfully even if the error showed up. Does it sound reasonable to continue with this error? ", "> > Facing the same error on Colab, strangely enough with a lower batch size the error doesn't actually stop the training, so I'm managing with a batch size of 4 (8 takes up more the 16GB of VRAM in my case)\n> \n> For me it's the same. The training was done successfully even if the error showed up. Does it sound reasonable to continue with this error? \n\nI've had no problem with the model after training so I think you can continue" ]

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[ "@agoeroeg Thanks for reporting the issue.\r\n\r\nI tried to replicate the issue in TF v2.3 and and in the latest TF v2.11 using a [simple MNIST convnet](https://keras.io/examples/vision/mnist_convnet/) .\r\n\r\nI have observed the reported behaviour in TF 2.3 where as in TF 2.11 I was able to get the `initial_value_threshold` work with the given value. \r\n\r\nPlease find the gist of TF 2.3 [here](https://colab.research.google.com/gist/pjpratik/1fb72958bcf1ff03ff61634f9e3cfe03/59577_2-3.ipynb) and gist of TF 2.11 [here](https://colab.research.google.com/gist/pjpratik/6feee0f23ba7e67e1ca3b8a8868e73b5/59577_2-11.ipynb) and let us know if it helps. Thanks!", "This issue has been automatically marked as stale because it has no recent activity. It will be closed if no further activity occurs. Thank you.\n", "Closing as stale. Please reopen if you'd like to work on this further.\n", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59577\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59577\">No</a>\n" ]

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[ "Hi, @maybeLee \r\n\r\nI was able to replicate the issue on Google Colab and I tried with latest stable version of Tensorflow `2.11` and also tried with `tf-nightly-2.13.0-dev20230207` but it's returning `inf` and for your reference I have added [gist file](https://colab.research.google.com/gist/gaikwadrahul8/dafdf6ccd4560cc198c313fe9ad78551/-59576.ipynb) so it seems like we have to dig more into this issue, we will update soon here and thank you for noticing the issue. Thank you!", "Hi, @gaikwadrahul8 \r\n\r\nI confirm your reproduced result. Following the MatLab's documentation: https://www.mathworks.com/help/matlab/ref/erfinv.html, PyTorch and Scipy's result, I think `tf.math.erfinv` outputs NaN instead of `inf` would be better. The reason is that: `inf` may be converted to other normal values (e.g., `tf.math.reciprocal(tf.math.erfinv(1.1))=0.0`), then the **problem (i.e., tf.math.erfinv receives an invalid input and its output should not be relied on) is hidden.** \r\n\r\nInstead, if erfinv outputs NaN, the problem will not be hidden (e.g., `tf.math.reciprocal(tf.math.erfinv(1.1)=NaN`, which is my expectation when using the tf.math.erfinv).", "Hi, @sachinprasadhs \r\n\r\nCould you please look into this issue? Thank you!", "TensorFlow's behavior with the erfinv function differs from other libraries like PyTorch and SciPy when handling invalid input values.\r\n\r\nTo work around this issue and achieve consistent behavior across libraries, you can use tf.where along with tf.math.is_finite to replace Inf values with NaN. Here's an example:\r\n```\r\nimport tensorflow as tf\r\n\r\ndef erfinv_with_nan(input_tensor):\r\n erfinv_result = tf.math.erfinv(input_tensor)\r\n return tf.where(tf.math.is_finite(erfinv_result), erfinv_result, float('nan'))\r\n\r\ninput_value = tf.Variable(1.1)\r\nresult = erfinv_with_nan(input_value)\r\nprint(result)\r\n\r\n```\r\nThis will output below\r\n`tf.Tensor(nan, shape=(), dtype=float32)`\r\n\r\nThis way, you can ensure that the output is consistent with the behavior observed in other libraries like PyTorch and SciPy.\r\n\r\nPlease note that this is a workaround and not a fix for the inconsistency in TensorFlow's implementation of erfinv. You can consider raising an issue on TensorFlow's GitHub repository to bring it to the developers' attention.", "This should now be fixed with the latest Eigen update.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59576\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59576\">No</a>\n" ]

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[ "@ralbertazzi \r\nThis issue is closed when the [PR](https://github.com/tensorflow/tensorflow/pull/59617) is merged.\r\n\r\nThank you !", "I think we have some clean up to do before getting rid of six; let me get back to you on this.", "Hi, thanks for bringing this to our attention! Yeah, we should definitely get rid of six, but we have some lingering places in the codebase where it's still in use, which is my blocker for approving the merge. Feel free to add that to this PR, or we also can get rid of the usages, although no promises on the timeline for that, unfortunately." ]

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[ "need to work with a counterpart change in mlir: https://reviews.llvm.org/D143312", "close this PR as discussed that the choice of using the operand type for those arguments has a better chance to support dynamic-shaped model." ]

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[ "I think it is related to the hostlist expansion, fix here #58033, because your nodelist is:\r\n\r\n```\r\nnode[01-04]\r\n```\r\nBut Tensorflow got:\r\n\r\n```\r\nnode1, node2, node3 and node4.\r\n```\r\n\r\nInstead of:\r\n\r\n```\r\nnode01, node02, node03 and node04.\r\n```\r\n", "> I think it is related to the hostlist expansion, fix here #58033, because your nodelist is:\r\n> \r\n> ```\r\n> node[01-04]\r\n> ```\r\n> \r\n> But Tensorflow got:\r\n> \r\n> ```\r\n> node1, node2, node3 and node4.\r\n> ```\r\n> \r\n> Instead of:\r\n> \r\n> ```\r\n> node01, node02, node03 and node04.\r\n> ```\r\n\r\nGood find! That was indeed an issue for me, so updated the file on each device for tensorflow/python/distribute/cluster_resolver/slurm_cluster_resolver.py, but the process still hangs. \r\n\r\nnew output:\r\n\r\n```shell\r\n2023-02-06 03:16:26.130945: I tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc:447] Started server with target: grpc://node01:15000\r\n2023-02-06 03:16:26.183769: I tensorflow/core/distributed_runtime/coordination/coordination_service.cc:502] /job:worker/replica:0/task:0 has connected to coordination service. Incarnation: 4221482841861972805\r\n2023-02-06 03:16:26.188286: I tensorflow/core/distributed_runtime/coordination/coordination_service_agent.cc:277] Coordination agent has successfully connected.\r\n2023-02-06 03:16:39.871488: I tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc:447] Started server with target: grpc://node02:15000\r\n2023-02-06 03:16:40.414216: I tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc:447] Started server with target: grpc://node04:15000\r\n2023-02-06 03:16:45.344939: I tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc:447] Started server with target: grpc://node03:15000\r\n```", "Fixed the issue. All host config files except the master on my machines were managed by a service, so on reboot the configurations for ip-hostname associations were removed.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59573\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59573\">No</a>\n" ]

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[ "@dazheyizu We see that the issue [template]( https://github.com/tensorflow/tensorflow/issues/new?assignees=&labels=&template=tensorflow_issue_template.yaml) has not been filled.\r\n\r\nCould you please do so as it helps us analyze the issue (TF version, steps followed before you ran into this error or stand alone code/colab gist to reproduce the issue faced). Thanks!", "This issue has been automatically marked as stale because it has no recent activity. It will be closed if no further activity occurs. Thank you.\n", "Closing as stale. Please reopen if you'd like to work on this further.\n", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59571\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59571\">No</a>\n" ]

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[ "Hi, @trickiwoo \r\n\r\nApologize for the delay and I was able to replicate the same issue on `Ubuntu 20.04.4 LTS` with `tf-nightly-2.13.0-dev20230206 `and even on Google Colab also, here is [gist file](https://colab.research.google.com/gist/gaikwadrahul8/cfcda98f2a4b78077ead45364fd6a2d3/-59570-tf-nightly.ipynb) for your reference and I have added screenshot below and You are sending invalid inputs to the API and the API returns a custom error back you can refer this [comment](https://github.com/tensorflow/tensorflow/issues/59350#issuecomment-1399378528) on similar error issue. Thank you!\r\n\r\n\r\n", "This issue has been automatically marked as stale because it has no recent activity. It will be closed if no further activity occurs. Thank you.\n", "@gaikwadrahul8 Thanks for your reply!", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59570\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59570\">No</a>\n" ]

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[ "Hi @joesho112358 It looks like your PR relates to the Keras component. Please submit it to the github.com/keras-team/keras repository instead. Thankyou.\r\n@fchollet, @qlzh727" ]

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[ "Hi @MarkDaoust Can you please review this PR ? Thank you!" ]

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[ "Tagging @penpornk and @cantonios.", "Eigen test also breaks after eigen update.\r\n\r\nBefore:\r\nExecuted 4597 out of 4623 tests: 4050 tests pass, 26 fail to build and 547 fail locally.\r\n\r\nAfter:\r\nExecuted 2640 out of 4629 tests: 2227 tests pass, 1989 fail to build and 413 fail locally.", "@ke1ding Thanks for letting me know. Someone must be defining `EIGEN_NO_DEBUG`, and then using asserts somewhere without first including `<cassert>`.\r\n\r\nPreviously, Eigen blindly included the `<cassert>` header unconditionally. However, we found out there are ODR issues with using `assert` in header files (Eigen being a header-only library) that prevent it from compiling with C++20 modules. So Eigen now completely disable asserts if `EIGEN_NO_DEBUG` is defined, and we no longer include the header.\r\n\r\nIt seems like some files within TensorFlow use plain `assert(...)` without first including the header themselves. I'll fix these. In the meantime, you can check your build flags to remove `EIGEN_NO_DEBUG`.", "@cantonios Thanks", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59563\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59563\">No</a>\n" ]

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[ "I ran your code, and benchmarked the tflite file using tensorflow/lite/tools/benchmark:benchmark_model --use_gpu=true. It ran fine on my machine. One possible issue might be if you have not synced your runtime tensorflow to include this commit: https://github.com/tensorflow/tensorflow/commit/642ccc0828a3ff8a6187f4d61f0c727602990596", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59560\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59560\">No</a>\n" ]

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[ "The RecordRandomReader operation is part of the TensorFlow IO library, which provides support for reading data from various file formats and data sources.\r\n\r\nIf you're encountering issues with the RecordRandomReader operation, here are a few steps you can try to resolve the issue:\r\n\r\nEnsure that the data source specified in the RecordRandomReader operation is correct and accessible.\r\n\r\nCheck that the format of the data source is compatible with the RecordRandomReader operation. The RecordRandomReader operation is designed to read records from a data source, so it's important to make sure that the data source is formatted as records.\r\n\r\nIf you're encountering issues with the TensorFlow IO library, you may want to check the TensorFlow IO GitHub repository for any known issues and potential solutions.\r\n\r\nIf you're encountering an error message or unexpected behavior, try to provide a minimal, reproducible example that demonstrates the issue. This will help others to understand and diagnose the issue more effectively.\r\n\r\nNote: These steps are general and may not resolve the issue for all cases. If the issue persists, I recommend reaching out to TensorFlow support or the TensorFlow user community for further assistance.\r\n\r\n\r\n\r\n\r\n", "Hi @saurabhmj11 - thanks for looking into this. I was refering to `tensorflow.python.lib.io._pywrap_record_io.RandomRecordReader`. Using this reader as part of a graph yields the following exception, which leads me to believe that it does only work in eager mode (or wrapped as py_function). \r\n\r\n\r\n" ]

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[ "Please don't do multiple cherrypicks in the same PR as it will be harder to bisect if the build breaks" ]

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