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tensorflow/c/eager/unified_api_testutil.h
TestScalarTensorHandle<T, datatype>(eager_ctx, value); *tensor = unwrap(TF_CreateAbstractTensorFromEagerTensor(input_eager, status.get())); return absl::OkStatus(); } // Places data from `t` into *result_tensor. Status GetValue(AbstractTensorHandle* t, TF_Tensor** result_tensor); } // namespace tensorflow
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tensorflow/c/c_api_test.cc
EXPECT_EQ(0, TF_OperationNumControlOutputs(neg)); EXPECT_EQ(0, TF_OperationGetControlOutputs(neg, control_ops, 100)); // Import it again, with an input mapping, return outputs, and a return // operation, into the same graph. TF_DeleteImportGraphDefOptions(opts); opts = TF_NewImportGraphDefOptions(); TF_ImportGraphDefOptionsSetPrefix(opts, "imported2"); TF_ImportGraphDefOptionsAddInputMapping(opts, "scalar", 0, {scalar, 0});
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tensorflow/api_template.__init__.py
`tf` instead of `tensorflow`, following the common practice of importing TensorFlow via the command `import tensorflow as tf`. The primary function of this module is to import all of the public TensorFlow interfaces into a single place. The interfaces themselves are located in sub-modules, as described below. Note that the file `__init__.py` in the TensorFlow source code tree is actually
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tensorflow/c/c_api.cc
const Graph& graph = session->graph->graph; const string& mutation_warning = session->graph->sessions[session]; if (!mutation_warning.empty()) { // TODO(b/74949947): turn this back into an error status LOG(WARNING) << mutation_warning; session->graph->sessions[session].clear(); } const auto num_nodes = graph.num_node_ids(); if (session->last_num_graph_nodes < num_nodes) {
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CONTRIBUTING.md
initiates CI/CD tests. - We can't move forward if these tests fail. - In such situations, we may request you to make further changes to your PR for the tests to pass. - Once the tests pass, we now bring all the code into the internal code base, using a job called "copybara". **5. Copy to Google Internal codebase and run internal CI** - Once the PR is in the Google codebase, we make sure it integrates well with
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tensorflow/c/eager/c_api_experimental.h
// Disables only graph collection in RunMetadata on the functions executed from // this context. TF_CAPI_EXPORT extern void TFE_ContextDisableGraphCollection(TFE_Context* ctx); // TODO(fishx): Move these monitoring APIs into a separate file. // ----------------------------------------------------------------------------- // Monitoring Counter APIs. // These APIs de-templated monitoring Counter for swig.
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RELEASE.md
* TensorFlow Debugger (tfdbg) CLI: ncurses-based CLI for tfdbg v1 was removed.
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tensorflow/c/eager/c_api_unified_experimental_graph.cc
auto& kUnknownDim = shape_inference::InferenceContext::kUnknownDim; auto& kUnknownRank = shape_inference::InferenceContext::kUnknownRank; // GraphTensor wraps a `TF_Output`, i.e. a pointer to TF_Operation and the index // into the list of outputs for the operation. class GraphTensor : public TracingTensorHandle { public: explicit GraphTensor(TF_Output output, TF_Graph* graph) : TracingTensorHandle(kGraph), output_(output), graph_(graph) {}
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tensorflow/BUILD
) config_setting( name = "with_xla_support", define_values = {"with_xla_support": "true"}, visibility = ["//visibility:public"], ) # By default, XLA GPU is compiled into tensorflow when building with # --config=cuda even when `with_xla_support` is false. The config setting # here allows us to override the behavior if needed. config_setting( name = "no_xla_deps_in_cuda",
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ci/official/utilities/code_check_full.bats
EOF # Get the full list of files and targets which get included into the pip # package bazel cquery --keep_going 'deps(//tensorflow/tools/pip_package:wheel)' | sort -u > $BATS_TEST_TMPDIR/pip_deps # Find all Python py_test targets not tagged "no_pip" or "manual", excluding # any targets in ignored packages. Combine this list of targets into a bazel # query list (e.g. the list becomes "target+target2+target3")
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