const auto& iter = fdef.ret().find(arg.name());
      if (iter != fdef.ret().end()) {
        const auto& v = a_edges.insert({iter->second, arg.name()});
        ASSERT_TRUE(v.second) << "Duplicate edge " << iter->second << " -> "
                              << arg.name() << ". fdef: " << fdef.DebugString();
      } else {
        const auto& v = a_edges.insert({arg.name(), arg.name()});

    job_def->mutable_tasks()->insert(
        {i, tensorflow::strings::StrCat("localhost:", port)});
  }
  auto* config = server_def.mutable_default_session_config();
  config->mutable_experimental()->set_collective_group_leader(
      tensorflow::strings::StrCat("/job:", job_name, "/replica:0/task:", 0));
  auto* rewrite_options =
      config->mutable_graph_options()->mutable_rewrite_options();

    numerical_grad.reset(numerical_grad_raw);
  }

  TF_Tensor* numerical_tensor;
  s = GetValue(numerical_grad.get(), &numerical_tensor);
  ASSERT_EQ(errors::OK, s.code()) << s.message();
  auto num_elem_numerical = TF_TensorElementCount(numerical_tensor);
  ASSERT_EQ(num_elem_numerical, num_grad);

  float* dnumerical = new float[num_elem_numerical]{0};
  memcpy(&dnumerical[0], TF_TensorData(numerical_tensor),

  // We can not pass a lambda with capture as a function pointer to
  // `TF_StartThread`, so we have to wrap `Prune` inside a static function.
  static void PruneThread(void* param) {
    auto ram_file_block_cache = static_cast<RamFileBlockCache*>(param);
    ram_file_block_cache->Prune();
  }

 private:
  /// The size of the blocks stored in the LRU cache, as well as the size of the

    // Create input_shapes.
    TF_ShapeAndTypeList* input_shapes =
        TF_NewShapeAndTypeList(input_shapes_vec.size());
    for (size_t i = 0; i < input_shapes_vec.size(); ++i) {
      const auto& input_shape = input_shapes_vec[i];
      if (input_shape.has_value()) {
        TF_ShapeAndTypeListSetShape(input_shapes, i, input_shape->data(),
                                    input_shape->size());
      } else {

absl-py==2.1.0 \
    --hash=sha256:526a04eadab8b4ee719ce68f204172ead1027549089702d99b9059f129ff1308 \
    --hash=sha256:7820790efbb316739cde8b4e19357243fc3608a152024288513dd968d7d959ff
    # via
    #   keras-nightly
    #   tb-nightly
astor==0.7.1 \
    --hash=sha256:95c30d87a6c2cf89aa628b87398466840f0ad8652f88eb173125a6df8533fb8d \
    --hash=sha256:fb503b9e2fdd05609fbf557b916b4a7824171203701660f0c55bbf5a7a68713e
    # via -r requirements.in
astunparse==1.6.3 \

    *   **x86 CPUs**:
        [oneDNN](https://github.com/tensorflow/community/blob/master/rfcs/20210930-enable-onednn-ops.md)
        bfloat16 auto-mixed precision grappler graph optimization pass has been
        renamed from `auto_mixed_precision_mkl` to
        `auto_mixed_precision_onednn_bfloat16`. See example usage

///
/// Since filesystem plugins are outside of TensorFlow's code tree, they are not
/// tied with TensorFlow releases and should have their own versioning metadata
/// in addition with the data discussed in this section. Each plugin author can
/// use a custom scheme, but it should only relate to changes in plugin code.
/// This section only touches metadata related to the versioning of this
/// interface that is shared by all possible plugins.
///

    int64_t op_id = op_id_it->second;
    auto op_it = op_tape->find(op_id);
    auto result_op_it = result.op_tape.find(op_id);
    if (op_id == -1 || op_it == op_tape->end() ||
        result_op_it != result.op_tape.end()) {
      continue;
    }
    CHECK(result.op_tape.emplace(op_id, op_it->second).second);
    for (auto it : op_it->second.input_tensor_id) {
      auto count_it = result.tensor_usage_counts.find(it);

  TF_Tensor* analytical_tensor;
  auto s = GetValue(t, &analytical_tensor);
  ASSERT_EQ(errors::OK, s.code()) << s.message();

  int64_t num_elem_analytical = 1;
  auto num_dims_analytical = TF_NumDims(analytical_tensor);
  ASSERT_EQ(dims.size(), num_dims_analytical);
  for (int j = 0; j < num_dims_analytical; j++) {
    auto dim_analytical = TF_Dim(analytical_tensor, j);