}

void TF_ImportGraphDefResultsReturnOperations(TF_ImportGraphDefResults* results,
                                              int* num_opers,
                                              TF_Operation*** opers) {
  *num_opers = results->return_nodes.size();
  *opers = results->return_nodes.data();
}

void TF_ImportGraphDefResultsMissingUnusedInputMappings(

    -   Uses `pycpp.sh`
-   Presubmit jobs (Run on every GitHub PR)
    -   Uses `pycpp.sh`, `code_check_changed_files.sh`

These "env" files match up with an environment matrix that roughly covers:

-   Different Python versions
-   Linux, MacOS, and Windows machines (these pool definitions are internal)
-   x86 and arm64
-   CPU-only, or with NVIDIA CUDA support (Linux only), or with TPUs

  EXPECT_EQ(0, return_outputs[1].index);

  // Check return operation
  TF_Operation** return_opers;
  int num_return_opers;
  TF_ImportGraphDefResultsReturnOperations(results, &num_return_opers,
                                           &return_opers);
  ASSERT_EQ(1, num_return_opers);
  EXPECT_EQ(scalar2, return_opers[0]);  // not remapped

  TF_DeleteImportGraphDefResults(results);

// Performs various checks.
Status ComputeBodyNodes(
    const TF_Graph* fn_body, const char* fn_name, int num_opers,
    const TF_Operation* const* opers,
    const std::unordered_map<const Node*, std::vector<int>>& input_nodes,
    std::vector<const Node*>* body_nodes)
    TF_EXCLUSIVE_LOCKS_REQUIRED(fn_body->mu) {
  if (num_opers == -1) {
    for (const Node* node : fn_body->graph.op_nodes()) {
      const auto& iter = input_nodes.find(node);

// TF_ImportGraphDefOptionsAddReturnOperation(). The number of fetched
// operations is returned in `num_opers`. The array of return operations is
// returned in `opers`. `*opers` is owned by and has the lifetime of `results`.
TF_CAPI_EXPORT extern void TF_ImportGraphDefResultsReturnOperations(
    TF_ImportGraphDefResults* results, int* num_opers, TF_Operation*** opers);

// Fetches any input mappings requested via

  std::unique_ptr<TF_Function, decltype(&TF_DeleteFunction)> function(
      TF_GraphToFunction(
          /* fn_body */ body.get(), /* fn_name */ function_name,
          /* append_hash_to_fn_name */ 0, /* num_opers */ 2,
          /* opers */ operations, /* ninputs */ 1, /* inputs */ &x,
          /* noutputs */ 1, /* outputs */ &y, /* output_names */ &output_name,
          /* opts */ nullptr, /* description */ "", /* status */ status),

    }
    return out;
  }

  void Define(int num_opers, const std::vector<TF_Operation*>& opers,
              const std::vector<TF_Operation*>& inputs,
              const std::vector<TF_Operation*>& outputs,
              const std::vector<string>& output_names,
              bool expect_failure = false) {
    DefineT(num_opers, opers, ToOutput(inputs), ToOutput(outputs), output_names,
            expect_failure);

vulnerabilities to the vendor of the affected hardware accelerator.

## Reporting vulnerabilities

### Vulnerabilities in TensorFlow

This document covers different use cases for TensorFlow together with comments
whether these uses were recommended or considered safe, or where we recommend
some form of isolation when dealing with untrusted data. As a result, this

  TF_Output inputs[] = {{feed, 0}};
  TF_Output outputs[] = {{neg, 0}};
  *func = TF_GraphToFunction(func_graph.get(), name, append_hash, -1,
                             /*opers=*/nullptr, 1, inputs, 1, outputs,
                             /*output_names=*/nullptr,
                             /*opts=*/nullptr, description, s.get());
  ASSERT_EQ(TF_OK, TF_GetCode(s.get())) << TF_Message(s.get());

Nishant Agrawal, Petr Janda, Yuncheng Li, @raix852, Robert Rose,
@Robin-des-Bois, Rohit Girdhar, Sam Abrahams, satok16, Sergey Kishchenko, Sharkd
Tu, @shotat, Siddharth Agrawal, Simon Denel, @sono-bfio, SunYeop Lee, Thijs
Vogels, @tobegit3hub, @Undo1, Wang Yang, Wenjian Huang, Yaroslav Bulatov, Yuan
Tang, Yunfeng Wang, Ziming Dong

We are also grateful to all who filed issues or helped resolve them, asked and