};

struct LoggedTensor {
  TFE_TensorHandle* tensor;
  LoggedTensor() = delete;
  explicit LoggedTensor(TFE_TensorHandle* tensor) : tensor(tensor) {}
  ~LoggedTensor() { TFE_DeleteTensorHandle(tensor); }
};

int64_t LoggedTensorDim(void* data, int dim_index, TF_Status* status) {
  return TFE_TensorHandleDim(reinterpret_cast<LoggedTensor*>(data)->tensor,
                             dim_index, status);
}

- Vendor: updated system-validators to v1.9.0 ([#128149](https://github.com/kubernetes/kubernetes/pull/128149), [@neolit123](https://github.com/neolit123)) [SIG Cluster Lifecycle and Node]

### Documentation

name === modifierName\n  );\n}\n","/**\n * Get the prefixed supported property name\n * @method\n * @memberof Popper.Utils\n * @argument {String} property (camelCase)\n * @returns {String} prefixed property (camelCase or PascalCase, depending on the vendor prefix)\n */\nexport default function getSupportedPropertyName(property) {\n  const prefixes = [false, 'ms', 'Webkit', 'Moz', 'O'];\n  const upperProp = property.charAt(0).toUpperCase() + property.slice(1);\n\n  for (let i = 0; i < prefixes.length;...

    TF_DeleteBuffer(op_list_buffer);
  }
}

void TestEncodeDecode(int line, const std::vector<string>& data) {
  const int64_t n = data.size();
  absl::Status status;
  for (const std::vector<int64_t>& dims :
       std::vector<std::vector<int64_t>>{{n}, {1, n}, {n, 1}, {n / 2, 2}}) {
    // Create C++ Tensor
    Tensor src(tensorflow::DT_STRING, TensorShape(dims));
    for (int64_t i = 0; i < src.NumElements(); ++i) {

  // Process inputs.
  std::vector<tensorflow::OutputTensor> input_tensors;
  std::unordered_map<const Node*, std::vector<int>> input_nodes;
  status->status = tensorflow::ProcessInputs(fn_body, fn_name, ninputs, inputs,
                                             &input_tensors, &input_nodes);
  if (TF_GetCode(status) != TF_OK) return nullptr;

  // Process outputs.
  std::vector<tensorflow::OutputTensor> output_tensors;

    : reader_(nullptr),
      v2_reader_(nullptr),
      var_to_shape_map_(nullptr),
      var_to_data_type_map_(nullptr) {
  // Depending on whether this is a V2 ckpt, initializes "reader_" or
  // "v2_reader_".
  std::vector<string> v2_path;
  if (Env::Default()->GetMatchingPaths(MetaFilename(filename), &v2_path).ok() &&
      !v2_path.empty()) {
    v2_reader_.reset(
        new BundleReader(Env::Default(), filename /* prefix to a V2 ckpt */));

  std::vector<tensorflow::string> tensor_id_data;
};

struct TF_ImportGraphDefResults {
  std::vector<TF_Output> return_tensors;
  std::vector<TF_Operation*> return_nodes;
  std::vector<const char*> missing_unused_key_names;
  std::vector<int> missing_unused_key_indexes;

  // Backing memory for missing_unused_key_names values.
  std::vector<tensorflow::string> missing_unused_key_names_data;

        "TFE_TensorHandleDeviceMemorySize may not be called on a ",
        handle->TypeString(), " tensor handle.");
    return 0;
  }
  const tensorflow::Tensor* tensor;
  status->status = handle->Tensor(&tensor);
  if (!status->status.ok()) {
    return 0;
  }
  return tensor->TotalBytes();
}

TFE_Op* TFE_NewOp(TFE_Context* ctx, const char* op_or_function_name,

    std::vector<TF_Output> graph_outputs;
    graph_outputs.reserve(outputs->outputs.size());
    for (auto* abstract_output : outputs->outputs) {
      GraphTensor* output = dyn_cast<GraphTensor>(abstract_output);
      if (!output) {
        return errors::Unimplemented(
            "Returning a non-graph tensor from a function has not "
            "been implemented yet.");

==============================================================================*/

#ifndef TENSORFLOW_C_EAGER_C_API_UNIFIED_EXPERIMENTAL_INTERNAL_H_
#define TENSORFLOW_C_EAGER_C_API_UNIFIED_EXPERIMENTAL_INTERNAL_H_

#include <vector>

#include "tensorflow/c/c_api.h"
#include "tensorflow/c/conversion_macros.h"
#include "tensorflow/c/eager/abstract_context.h"
#include "tensorflow/c/eager/abstract_operation.h"