[[0,0],    *   [[0,0],    =   [[0,0],
     [0,0]]         [0,0]]         [0,0]]
  */

  // Build an abstract input tensor.
  int64_t dims[] = {2, 2};  // Matrices will be 2 x 2
  int num_dims = sizeof(dims) / sizeof(dims[0]);

  float vals[] = {0.0f, 0.0f, 0.0f, 0.0f};
  TFE_Context* eager_ctx = TF_ExecutionContextGetTFEContext(ctx, status.get());
  TFE_TensorHandle* t =

		// if modification time is the only timestamp being encoded.
		var mbuf [9]byte // 2*SizeOf(uint16) + SizeOf(uint8) + SizeOf(uint32)
		mt := uint32(fh.Modified.Unix())
		eb := writeBuf(mbuf[:])
		eb.uint16(extTimeExtraID)
		eb.uint16(5)  // Size: SizeOf(uint8) + SizeOf(uint32)
		eb.uint8(1)   // Flags: ModTime
		eb.uint32(mt) // ModTime
		fh.Extra = append(fh.Extra, mbuf[:]...)
	}

                          std::unique_ptr<const void*[]>* ptrs,
                          std::unique_ptr<size_t[]>* lens) {
  ptrs->reset(new const void*[v.size()]);
  lens->reset(new size_t[v.size()]);
  for (size_t i = 0; i < v.size(); ++i) {
    (*ptrs)[i] = v[i].data();
    (*lens)[i] = v[i].size();
  }
}

class CApiColocationTest : public ::testing::Test {
 protected:

  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);

                            systemHelper.getCurrentTimeAsLong() - execTime, MemoryUtil.byteCountToDisplaySize(docList.getContentSize()),
                            MemoryUtil.getMemoryUsageLog());
                } else {
                    logger.info("Sent {}  docs (Doc:{send {}ms}, {})", docList.size(), systemHelper.getCurrentTimeAsLong() - execTime,

      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.
  //

}

const char* TF_GraphDebugString(TF_Graph* graph, size_t* len) {
  tensorflow::mutex_lock c(graph->mu);
  const auto& debug_str = graph->graph.ToGraphDefDebug().DebugString();
  *len = debug_str.size();
  char* ret = static_cast<char*>(malloc(*len + 1));
  memcpy(ret, debug_str.c_str(), *len + 1);
  return ret;
}

char* TF_FunctionDebugString(TF_Function* func, size_t* len) {

  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])

To access a struct, union, or enum type directly, prefix it with
struct_, union_, or enum_, as in C.struct_stat. The size of any C type
T is available as C.sizeof_T, as in C.sizeof_struct_stat. These
special prefixes means that there is no way to directly reference a C
identifier that starts with "struct_", "union_", "enum_", or
"sizeof_", such as a function named "struct_function".
A workaround is to use a "#define" in the preamble, as in

a <a href="#Run_time_panics">run-time panic</a> occurs.
</p>

<h3 id="Size_and_alignment_guarantees">Size and alignment guarantees</h3>

<p>
For the <a href="#Numeric_types">numeric types</a>, the following sizes are guaranteed:
</p>

<pre class="grammar">
type                                 size in bytes

byte, uint8, int8                     1
uint16, int16                         2