Weekly GitHub Report for Tensorflow: October 13, 2025 - October 20, 2025 (12:05:58)

Weekly GitHub Report for Tensorflow

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Table of Contents

• I. News
  • 1.1. Recent Version Releases
  • 1.2. Other Noteworthy Updates
• II. Issues
  • 2.1. Top 5 Active Issues
  • 2.2. Top 5 Stale Issues
  • 2.3. Open Issues
  • 2.4. Closed Issues
  • 2.5. Issue Discussion Insights

• III. Pull Requests
  • 3.1. Open Pull Requests
  • 3.2. Closed Pull Requests
  • 3.3. Pull Request Discussion Insights

• IV. Contributors
  • 4.1. Contributors

I. News

1.1 Recent Version Releases:

The current version of this repository is v2.19.0

1.2 Version Information:

Released on March 5, 2025, TensorFlow version 2.19.0 introduces breaking changes to the tf.lite API, including the deprecation of tf.lite.Interpreter in favor of ai_edge_litert.interpreter and changes to certain C++ API constants for improved compatibility. Key updates also include runtime support for the bfloat16 data type in the tfl.Cast operation, alongside the discontinuation of standalone libtensorflow package publishing.

II. Issues

2.1 Top 5 Active Issues:

We consider active issues to be issues that that have been commented on most frequently within the last week. Bot comments are omitted.

1. Add the limitation on types transferred in func tf.raw_ops.ComplexAbs(): This issue reports a documentation bug in TensorFlow's tf.raw_ops.ComplexAbs() function, where the limitations on the types that can be transferred are not clearly stated in the docstring. The user demonstrates that certain type combinations cause errors and requests that these constraints be explicitly documented to prevent confusion.

   • The comments acknowledge the issue and highlight that a pull request has already been submitted to address the documentation gap; the maintainers express appreciation for the contribution and confirm that merging the PR will resolve the problem.
   • Number of comments this week: 2

2. Add the limitation on types in func tf.raw_ops.Imag(): This issue addresses a documentation bug in TensorFlow's tf.raw_ops.Imag() function, specifically the need to clearly state the limitations on the data types supported by the function's Tout parameter. The reporter demonstrates that certain type combinations cause errors and requests that these constraints be explicitly documented to prevent confusion.

   • The comments acknowledge the issue and highlight that a pull request has already been submitted to fix the documentation. The maintainers express appreciation for the contribution and confirm that merging the PR will resolve the problem.
   • Number of comments this week: 2

3. TF_SYSTEM_LIBS doesn't work: This issue reports a build and installation problem with TensorFlow versions 2.20 and 2.21 nightly on Arch Linux, where setting the TF_SYSTEM_LIBS environment variable does not work as expected. The user encounters errors related to missing headers and a failure in the nvcc_wrapper during the linking stage when attempting to build a CUDA-enabled TensorFlow wheel.

   • The single comment in the thread is a brief acknowledgment without additional troubleshooting or solutions provided.
   • Number of comments this week: 1

4. Tensorflow lite reference kernel ReduceSum overflows when network type not uint8: This issue reports an overflow problem in the TensorFlow Lite reference kernel for the ReduceSum operation when the network data type is not uint8, specifically affecting int8 inputs. The user identifies the bug in the reduce.cc file where the QuantizedMeanOrSum function incorrectly uses uint8 instead of the template type T, and provides a patch to fix this type mismatch.

   • The comment section contains a single inquiry asking if the issue is still open, with no further discussion or updates provided.
   • Number of comments this week: 1

5. TensorFlow NearestNeighborsOp Crash Due to Negative k Value: This issue reports a bug in TensorFlow version 2.18 where the NearestNeighbors operation crashes due to a negative value for the parameter k, which specifies the number of nearest neighbors to find. The crash occurs because TensorFlow expects shape dimensions to be non-negative, and passing k = -1 leads to an invalid argument error and a core dump.

   • The comment explains that the root cause is the invalid negative k value, which is mathematically incorrect for the operation, and provides a fix by changing k from -1 to 1, ensuring k is a positive integer within the valid range.
   • Number of comments this week: 1

2.2 Top 5 Stale Issues:

We consider stale issues to be issues that has had no activity within the last 30 days. The team should work together to get these issues resolved and closed as soon as possible.

1. TF-TRT Warning: Could not find TensorRT: This issue describes a problem where the user is encountering a warning that TensorRT cannot be found when running TensorFlow 2.15.0 on an Ubuntu 22.04 system with an NVIDIA RTX 3050 Ti GPU, CUDA 12.4, and the NVIDIA 535 driver. Despite multiple attempts to uninstall and reinstall TensorFlow and adjusting the GPU driver version, the user is unable to resolve the TensorRT detection error and is seeking assistance to fix this setup issue.
2. SystemError in tf.ensure_shape and tf.compat.v1.ensure_shape when dtype of shape is tf.uint64 and its value is too large.: This issue reports a bug in TensorFlow where using tf.ensure_shape or tf.compat.v1.ensure_shape with a shape tensor of data type tf.uint64 containing very large values close to 2^64 causes a SystemError and OverflowError. Specifically, when such large uint64 values are passed in eager execution mode, the functions fail with an internal error related to type checking, indicating improper handling of large unsigned 64-bit integers.
3. Feature Request: Integrate different Digital Signal Processing into tf.signal: This issue is a feature request proposing the integration of advanced digital signal processing (DSP) functionalities, similar to those found in the julius library, into TensorFlow's tf.signal module. The goal is to enhance TensorFlow's native capabilities for audio data augmentation, enabling researchers and developers to perform complex audio preprocessing and augmentation directly within the TensorFlow ecosystem, thereby improving workflow efficiency and model robustness.
4. [DOCS] Missing complex input for Round op: This issue reports a documentation bug concerning the Round operation in TensorFlow, where the official documentation states that complex tensors are supported as input, but in practice, attempting to use a complex tensor with Round results in an error. The user found that the operation does not work as expected on complex inputs and instead requires manually applying the Round function separately to the real and imaginary parts, indicating a discrepancy between the documented behavior and the actual implementation.
5. tf.raw_ops.Unbatch aborts with "Check failed: d < dims()": This issue reports a bug in TensorFlow version 2.17 where the operation tf.raw_ops.Unbatch causes the program to abort with a "Check failed: d < dims()" error. The problem occurs when running a specific code snippet on Linux Ubuntu 20.04.3 with Python 3.11.8, resulting in a core dump due to a failed tensor shape check.

2.3 Open Issues

This section lists, groups, and then summarizes issues that were created within the last week in the repository.

Issues Opened This Week: 14

Summarized Issues:

• Build and Compilation Issues: Multiple build failures occur due to missing providers in Bazel targets, environment variable malfunctions, and segmentation faults during model building. These issues affect different architectures and platforms, including riscv64, Arch Linux with CUDA, and Windows 10 with Ubuntu subsystem, causing build errors and runtime crashes.
• issues/102159, issues/102206, issues/102416

• Documentation Gaps in TensorFlow Ops: Several TensorFlow operations lack clear documentation on type constraints and output behaviors, leading to user confusion and errors. This includes missing type limitations for tf.raw_ops.ComplexAbs() and tf.raw_ops.Imag(), as well as unclear behavior descriptions for tf.raw_ops.SelfAdjointEigV2() and tf.signal.idct().
• issues/102343, issues/102349, issues/102352, issues/102418

• TensorFlow Functionality Bugs: Bugs in core TensorFlow functions cause crashes, incorrect outputs, or unexpected behavior. Examples include integer overflow in tf.raw_ops.UnsortedSegmentProd on GPU, deserialization failures of saved Keras models due to missing function registrations, and crashes in NearestNeighborsOp when given invalid parameters.
• issues/102383, issues/102475, issues/102492

• TensorFlow Lite Specific Issues: TensorFlow Lite faces problems such as overflow in the ReduceSum kernel due to template typing errors, warnings about unsupported 16 KB pages in native libraries, and potential NULL pointer dereference vulnerabilities in add operations. These issues impact both correctness and security of TFLite models.
• issues/102490, issues/102536, issues/102546

• Platform Support and Build Guidance Requests: There is a need for updated documentation and guidance on building TensorFlow on new CPU and OS combinations, including questions about local Python usage and cross-compilation. This reflects gaps in official instructions for unsupported or emerging platforms.
• issues/102411

2.4 Closed Issues

This section lists, groups, and then summarizes issues that were closed within the last week in the repository. This section also links the associated pull requests if applicable.

Issues Closed This Week: 4

Summarized Issues:

• DLL Load Failures and Import Errors: Several issues report DLL load failures causing TensorFlow's native runtime to fail initialization, resulting in ImportErrors when importing internal modules or using Keras. These errors occur across different TensorFlow versions and environments, indicating a recurring problem with native runtime loading on Windows platforms.
• issues/102210, [issues/102402](https://github.com/tensorflow/tensorflow/issues/102402]

• Model Loading and Access Permissions: One issue highlights a 403 Forbidden error when loading the YAMNet model from TensorFlow Hub using Node.js, despite no changes in the code. This suggests an external access or permission problem rather than an issue with TensorFlow itself.
• [issues/102158](https://github.com/tensorflow/tensorflow/issues/102158]

• Neural Network Model Implementation: An issue discusses implementing and training a simple neural network using TensorFlow's Keras API to learn a linear function y = 2x + 1. This focuses on basic model creation and training without reported errors.
• [issues/102354](https://github.com/tensorflow/tensorflow/issues/102354]

2.5 Issue Discussion Insights

This section will analyze the tone and sentiment of discussions within this project's open and closed issues that occurred within the past week. It aims to identify potentially heated exchanges and to maintain a constructive project environment.

Based on our analysis, there are no instances of toxic discussions in the project's open or closed issues from the past week.

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III. Pull Requests

3.1 Open Pull Requests

This section provides a summary of pull requests that were opened in the repository over the past week. The top three pull requests with the highest number of commits are highlighted as 'key' pull requests. Other pull requests are grouped based on similar characteristics for easier analysis. Up to 25 pull requests are displayed in this section, while any remaining pull requests beyond this limit are omitted for brevity.

Pull Requests Opened This Week: 8

Key Open Pull Requests

1. Add RISC-V 64-bit architecture support for TensorFlow builds: This pull request adds comprehensive support for building TensorFlow on the RISC-V 64-bit architecture by correcting the Python C/C++ toolchain provider structure to comply with rules_python requirements, including new toolchain implementations, build configurations, automated setup and verification scripts, detailed documentation, and workspace integration, thereby resolving the compilation error described in issue #102159.

• URL: pull/102203

• Merged: No

• Associated Commits: a6c38, bea76

2. absl::InitializeLog() must be called before logging.: This pull request addresses the issue of logging in the TensorFlow project by ensuring that absl::InitializeLog() is called before any logging occurs to prevent log messages from being written to STDERR and to eliminate the associated warning.

• URL: pull/102212

• Merged: No

• Associated Commits: ed8ad

3. Update CuDNN FrontEnd to match the used one with XLA that is compatible with CUDA 13: This pull request updates the CuDNN FrontEnd to align with the version used by XLA that is compatible with CUDA 13, resolving build errors caused by incompatibilities between CuDNN FrontEnd 1.11.0 and CUDA 13 by requiring at least CuDNN FrontEnd 1.12.0.

• URL: pull/102224

• Merged: No

• Associated Commits: e101c

Other Open Pull Requests

• AArch64 CPUIDInfo Initialization Bug Fix: This pull request fixes a bug in the AArch64 CPUIDInfo initialization by ensuring the Initialize() method is called during its creation. This prevents AArch64 builds from unnecessarily falling back to Eigen for many operations, improving performance and correctness.
• pull/102272

• 64-bit Integer Comparison Fix on MSVC Windows: This pull request addresses a platform-specific bug on MSVC Windows by specializing the minimum and maximum functors for int64_t. This ensures correct 64-bit integer comparisons and improves numerical stability and correctness in operations like tf.minimum and tf.maximum.
• pull/102327

• Type Limitations for Complex Number Operations: These pull requests add type limitations to TensorFlow functions handling complex numbers, specifically tf.raw_ops.ComplexAbs() and tf.raw_ops.Imag(). They address issues by restricting transferable types to prevent errors and improve function robustness.
• pull/102344, pull/102350

• Validation for argsort with Complex Data Types: This pull request adds validation to the np_math_ops.argsort function to raise a clear TypeError when complex64 or complex128 data types are used. It replaces a confusing internal error and includes test coverage to ensure this behavior is properly handled.
• pull/102468

3.2 Closed Pull Requests

This section provides a summary of pull requests that were closed in the repository over the past week. The top three pull requests with the highest number of commits are highlighted as 'key' pull requests. Other pull requests are grouped based on similar characteristics for easier analysis. Up to 25 pull requests are displayed in this section, while any remaining pull requests beyond this limit are omitted for brevity.

Pull Requests Closed This Week: 1

Key Closed Pull Requests

1. Reland #102094: fix 16kb alignment: This pull request relands a previous change to fix 16KB page alignment for the core JNI in the TensorFlow build system, correcting an accidental merge by force pushing a rebase that removes the unintended commit.

• URL: pull/102292

• Merged: Yes

• Associated Commits: b48d0

3.3 Pull Request Discussion Insights

This section will analyze the tone and sentiment of discussions within this project's open and closed pull requests that occurred within the past week. It aims to identify potentially heated exchanges and to maintain a constructive project environment.

Based on our analysis, there are no instances of toxic discussions in the project's open or closed pull requests from the past week.

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IV. Contributors

4.1 Contributors

Active Contributors:

We consider an active contributor in this project to be any contributor who has made at least 1 commit, opened at least 1 issue, created at least 1 pull request, or made more than 2 comments in the last month.

If there are more than 10 active contributors, the list is truncated to the top 10 based on contribution metrics for better clarity.

Contributor	Commits	Pull Requests	Issues	Comments
ILCSFNO	13	8	9	6
CodersAcademy006	11	10	0	9
mihaimaruseac	0	1	0	10
kshiteej-mali	7	1	0	0
codingWizard-Nikhil	5	2	0	0
Venkat6871	1	1	0	5
syan427	0	0	6	0
Ma-gi-cian	2	1	0	2
tinywisdom	0	0	5	0
khteh	0	0	4	1