EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  TF_Tensor* t = TFE_TensorHandleResolve(retvals[0], status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  float product[4] = {0};
  EXPECT_EQ(sizeof(product), TF_TensorByteSize(t));
  memcpy(&product[0], TF_TensorData(t), TF_TensorByteSize(t));
  TF_DeleteTensor(t);
  EXPECT_EQ(7, product[0]);
  EXPECT_EQ(10, product[1]);
  EXPECT_EQ(15, product[2]);

#include "tensorflow/c/tf_status.h"

// Implementation of a filesystem for POSIX environments.
// This filesystem will support `file://` and empty (local) URI schemes.

static void* plugin_memory_allocate(size_t size) { return calloc(1, size); }
static void plugin_memory_free(void* ptr) { free(ptr); }

// SECTION 1. Implementation for `TF_RandomAccessFile`
// ----------------------------------------------------------------------------

static std::unique_ptr<const T> CopyToCore(const T* plugin_ops,
                                           size_t plugin_size) {
  if (plugin_ops == nullptr) return nullptr;

  size_t copy_size = std::min(plugin_size, sizeof(T));
  auto core_ops = std::make_unique<T>();
  memset(core_ops.get(), 0, sizeof(T));
  memcpy(core_ops.get(), plugin_ops, copy_size);
  return core_ops;
}

// Registers one filesystem from the plugin.
//

TensorHandlePtr VectorFloatTensorHandle(const std::vector<float>& v,
                                        TF_Status* status) {
  const int num_bytes = v.size() * sizeof(float);
  float* values = new float[v.size()];
  memcpy(values, v.data(), num_bytes);
  int64_t dims = v.size();
  std::unique_ptr<TF_Tensor, decltype(&TF_DeleteTensor)> tensor(
      TF_NewTensor(TF_FLOAT, &dims, 1 /* num_dims */, values, num_bytes,

  TF_Tensor* t = TFE_TensorHandleResolve(retvals[0], status);
  ASSERT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);
  TFE_DeleteTensorHandle(retvals[0]);
  float sum = 0;
  EXPECT_EQ(sizeof(sum), TF_TensorByteSize(t));
  memcpy(&sum, TF_TensorData(t), TF_TensorByteSize(t));
  TF_DeleteTensor(t);
  EXPECT_EQ(sum, 6.0);

  TFE_DeleteTensorHandle(h0);
  TFE_DeleteTensorHandle(h1);

@@ -1609,7 +1609,7 @@ writezone(const char *const name, const char *const string)
 		}
 #define DO(field)	((void) fwrite(tzh.field, sizeof tzh.field, 1, fp))
 		tzh = tzh0;
-		(void) strncpy(tzh.tzh_magic, TZ_MAGIC, sizeof tzh.tzh_magic);
+		memcpy(tzh.tzh_magic, TZ_MAGIC, sizeof tzh.tzh_magic);
 		tzh.tzh_version[0] = ZIC_VERSION;
 		convert(eitol(thistypecnt), tzh.tzh_ttisgmtcnt);

  TF_Tensor* t = TFE_TensorHandleResolve(retval_task0, status);
  ASSERT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);
  TFE_DeleteTensorHandle(retval_task0);
  float product[4] = {0};
  EXPECT_EQ(sizeof(product), TF_TensorByteSize(t));
  memcpy(&product[0], TF_TensorData(t), TF_TensorByteSize(t));
  TF_DeleteTensor(t);
  EXPECT_EQ(7, product[0]);
  EXPECT_EQ(10, product[1]);
  EXPECT_EQ(15, product[2]);

constexpr int TF_RANDOM_ACCESS_FILE_OPS_ABI = 0;
constexpr size_t TF_RANDOM_ACCESS_FILE_OPS_SIZE =
    sizeof(TF_RandomAccessFileOps);
// LINT.ThenChange()

// LINT.IfChange(writable_file_ops_version)
constexpr int TF_WRITABLE_FILE_OPS_API = 0;
constexpr int TF_WRITABLE_FILE_OPS_ABI = 0;
constexpr size_t TF_WRITABLE_FILE_OPS_SIZE = sizeof(TF_WritableFileOps);
// LINT.ThenChange()

  std::unique_ptr<float[]> actual_values(new float[expected_values.size()]);
  EXPECT_EQ(sizeof(float) * expected_values.size(), TF_TensorByteSize(t));
  memcpy(actual_values.get(), TF_TensorData(t), TF_TensorByteSize(t));
  TF_DeleteTensor(t);

  for (int i = 0; i < expected_values.size(); i++) {
    EXPECT_EQ(expected_values[i], actual_values[i])

      const ParallelDevice& parallel_device,
      std::vector<TensorHandlePtr> components, absl::Span<const int64_t> shape,
      TF_Status* status);

  size_t num_tensors() const { return tensors_.size(); }
  TFE_TensorHandle* tensor(size_t index) const { return tensors_[index].get(); }

  // If the `shape` argument to `FromTensorHandles` is specified, returns that.
  //