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.github/ISSUE_TEMPLATE/tflite-converter-issue.md
### 2. Code Provide code to help us reproduce your issues using one of the following options: #### Option A: Reference colab notebooks 1) Reference [TensorFlow Model Colab](https://colab.research.google.com/gist/ymodak/e96a4270b953201d5362c61c1e8b78aa/tensorflow-datasets.ipynb?authuser=1): Demonstrate how to build your TF model.
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tensorflow/c/experimental/filesystem/plugins/gcs/cleanup.h
F release() { released_ = true; return std::move(f_); } bool is_released() const { return released_; } private: static_assert(!std::is_reference<F>::value, "F must not be a reference"); bool released_ = false; F f_; }; template <int&... ExplicitParameterBarrier, typename F, typename DecayF = typename std::decay<F>::type>
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tensorflow/c/eager/graph_function.h
~GraphFunction() override; // GraphFunction maybe stay alive for the duration of the returned // FunctionDef. Status GetFunctionDef(const FunctionDef** fdef) override; // Returns a shared reference to the wrapped function. absl::StatusOr<core::RefCountPtr<FunctionRecord>> GetFunctionRecord() override { return func_record_.GetNewRef(); } // For LLVM style RTTI.
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ci/official/README.md
# "rbe", if you have set up your system to use RBE (see further below). # # Here is how to choose your TFCI value: # 1. A Python version must come first, because other scripts reference it. # Ex. py39 -- Python 3.9 # Ex. py310 -- Python 3.10 # Ex. py311 -- Python 3.11 # Ex. py312 -- Python 3.12 # 2. Choose the platform, which corresponds to the version of TensorFlow to
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tensorflow/c/eager/abstract_function.h
AbstractFunctionKind getKind() const { return kind_; } // Returns the AbstractFunction as a FunctionDef. virtual Status GetFunctionDef(const FunctionDef**) = 0; // Returns a shared reference to the wrapped function. virtual absl::StatusOr<core::RefCountPtr<FunctionRecord>> GetFunctionRecord() = 0; private: const AbstractFunctionKind kind_; }; using AbstractFunctionPtr =
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ci/official/containers/linux_arm64/builder.devtoolset/gcc9-fixups.patch
dynamic_addr(symbol) = static_addr(symbol) + load_addr - The choice of symbol is arbitrary. The static address we obtain - by constructing a non GOT reference to the symbol, the dynamic - address of the symbol we compute using adrp/add to compute the - symbol's address relative to the PC. */ - - ElfW(Addr) static_addr; - ElfW(Addr) dynamic_addr; -
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ci/official/utilities/generate_index_html.sh
<li><a href="http://cs/f:learning/brain/testing/kokoro/$(echo "$KOKORO_JOB_NAME" | sed 's!tensorflow/!!g')">Codesearch - build definition & scripts</a></li> <li><a href="http://cs/$KOKORO_JOB_NAME">Codesearch - All references to this job</a></li> </ul> <h2>Non-Googler Links</h2> <ul> <li><a href="https://source.cloud.google.com/results/invocations/$KOKORO_BUILD_ID">ResultStore</a></li> </ul> </body></html>
Shell Script - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Fri Sep 29 20:26:13 GMT 2023 - 2.3K bytes - Viewed (0) -
.github/workflows/update-rbe.yml
# limitations under the License. # ============================================================================== # This Workflow updates tensorflow/tools/toolchains/remote_config/configs.bzl # to reference the most recent versions of the SIG Build Docker images. name: Update RBE Configs on: workflow_dispatch: permissions: contents: read jobs: rbe: name: Update RBE Configs
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.github/workflows/create_issue.js
let assignee_logins = assignees.map(x => x.login); assignee_logins.push(pr_resp.data.user.login); console.log(assignee_logins); // Create an new GH Issue and reference the Original PR const resp = await github.rest.issues.create({ owner, repo, assignees: assignee_logins, title: `Issue created for Rollback of PR #${pr_number}: ${pr_title}`,
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tensorflow/c/c_api_internal.h
struct TF_Library { void* lib_handle; TF_Buffer op_list; }; struct TF_Graph { TF_Graph(); mutable tensorflow::mutex mu; tensorflow::Graph graph TF_GUARDED_BY(mu); // Runs shape inference. tensorflow::ShapeRefiner refiner TF_GUARDED_BY(mu); // Maps from name of an operation to the Node* in 'graph'. std::unordered_map<tensorflow::string, tensorflow::Node*> name_map TF_GUARDED_BY(mu);
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