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.github/ISSUE_TEMPLATE/tflite-op-request.md
--- name: TensorFlow Lite Op Request about: Use this template for reporting Lite ops you are using or missing labels: 'comp:lite' --- **System information** - OS Platform and Distribution (e.g., Linux Ubuntu 16.04): - TensorFlow installed from (source or binary): - TensorFlow version (or github SHA if from source): **Provide the text output from tflite_convert** ``` # Copy and paste here ```
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ci/official/utilities/generate_index_html.sh
<li>Job ID: $KOKORO_BUILD_ID</li> <li>Current HEAD Piper Changelist, if any: cl/${KOKORO_PIPER_CHANGELIST:-not available}</li> <li>Pull Request Number, if any: ${KOKORO_GITHUB_PULL_REQUEST_NUMBER_tensorflow:- none}</li> <li>Pull Request Link, if any: <a href="$KOKORO_GITHUB_PULL_REQUEST_URL_tensorflow">${KOKORO_GITHUB_PULL_REQUEST_URL_tensorflow:-none}</a></li> <li>Commit: $KOKORO_GIT_COMMIT_tensorflow</li> </ul> <h2>Googlers-Only Links</h2> <ul>
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.github/ISSUE_TEMPLATE/tflite-other.md
Build/Install - Performance - Support - Feature Request - Documentation Feature Request - Documentation Bug - Others validations: required: true - type: markdown attributes: value: | Please make sure that this is a bug. As per our [GitHub Policy](https://github.com/tensorflow/tensorflow/blob/master/ISSUES.md),we only address code/doc bugs, performance issues, feature requests and build/installation issues on GitHub. - type: markdown
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ISSUES.md
If you open a GitHub Issue, here is our policy: 1. It must be a bug/performance issue or a feature request or a build issue or a documentation issue (for small doc fixes please send a PR instead). 1. Make sure the Issue Template is filled out. 1. The issue should be related to the repo it is created in. **Here's why we have this policy:** We want to focus on the work that benefits the whole community, e.g., fixing bugs and adding features. Individual support
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tensorflow/c/experimental/filesystem/plugins/gcs/ram_file_block_cache.cc
block->lra_iterator = lra_list_.begin(); block->timestamp = timer_seconds_(); } } }); // Loop until either block content is successfully fetched, or our request // encounters an error. absl::MutexLock l(&block->mu); TF_SetStatus(status, TF_OK, ""); while (true) { switch (block->state) { case FetchState::ERROR: // TF_FALLTHROUGH_INTENDED
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.github/workflows/issue-on-pr-rollback.yml
push: branches: - master permissions: {} jobs: create-issue-on-pr-rollback: runs-on: ubuntu-latest permissions: contents: read issues: write pull-requests: read if: | github.repository == 'tensorflow/tensorflow' && startsWith(github.event.head_commit.message, 'Rollback of PR #') steps: - name: Checkout repo
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tensorflow/c/eager/c_api_remote_test_util.h
#ifndef TENSORFLOW_C_EAGER_C_API_REMOTE_TEST_UTIL_H_ #define TENSORFLOW_C_EAGER_C_API_REMOTE_TEST_UTIL_H_ // Run a function containing a MatMul op and check its output. // If heavy_load_on_streaming_rpc is true, send some rpc requests before the one // which creates a remote input, to simulate a scenario that the remote input // is not ready when we start running an op or a function. void TestRemoteExecuteSilentCopies(bool async, bool remote, bool func,
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tensorflow/c/experimental/filesystem/plugins/gcs/ram_file_block_cache_test.cc
const size_t block_size = 16; int num_requests = 0; Notification notification; auto fetcher = [&num_requests, ¬ification, block_size]( const string& filename, size_t offset, size_t n, char* buffer, TF_Status* status) -> int64_t { EXPECT_EQ(n, block_size); EXPECT_EQ(offset, 0); num_requests++; memset(buffer, 'x', n); notification.Notify();
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tensorflow/c/experimental/filesystem/modular_filesystem_test.cc
// only once. Hence, this function keeping another static pointer to a vector // which contains only the schemes under test. // // Without this additional step, when there are schemes available but the user // only requests schemes that don't exist, first instantiation of the test would // filter out all the user provided schemes (as they are not registered) but // subsequent instantiations would return all registered schemes (since the
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CONTRIBUTING.md
pull requests are approved and pass continuous integration checks, a TensorFlow team member will apply `ready to pull` label to your change. This means we are working on getting your pull request submitted to our internal repository. After the change has been submitted internally, your pull request will be merged automatically on GitHub. If you want to contribute, start working through the TensorFlow codebase, navigate to the
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