[[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[:]...)
	}

  }

  TF_Tensor* FloatTensor2x2(const float* values) {
    const int64_t dims[2] = {2, 2};
    TF_Tensor* t = TF_AllocateTensor(TF_FLOAT, dims, 2, sizeof(float) * 4);
    memcpy(TF_TensorData(t), values, sizeof(float) * 4);
    return t;
  }

  TF_Operation* FloatConst2x2(TF_Graph* graph, TF_Status* s,
                              const float* values, const char* name) {

  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);

        }
        return response.getStatus().getStatus();
    }

    public long calculateDocumentSize(final Map<String, Object> dataMap) {
        return MemoryUtil.sizeOf(dataMap);
    }

    public void setMaxRetryCount(final int maxRetryCount) {
        this.maxRetryCount = maxRetryCount;
    }

    public void setDefaultRowSize(final int defaultRowSize) {

  DCHECK(num_dims >= 0) << "Number of dimensions cannot be negative!";
  shape.num_dims = num_dims;
  shape.dims = new int64_t[num_dims];
  memcpy(shape.dims, dims, sizeof(int64_t) * num_dims);
}

void TF_ShapeAndTypeListSetUnknownShape(TF_ShapeAndTypeList* shape_list,
                                        int index) {
  DCHECK(index >= 0 && index < shape_list->num_items);

    std::unique_ptr<TF_Tensor, decltype(&TF_DeleteTensor)> tensor(
        TF_NewTensor(
            datatype_enum, /*dims=*/nullptr, /*num_dims=*/0,
            device_value.release(), sizeof(DataType),
            [](void* data, size_t, void* arg) {
              delete reinterpret_cast<DataType*>(data);
            },
            nullptr),
        TF_DeleteTensor);

  TF_Tensor* t = TFE_TensorHandleResolve(handle, status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());
  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++) {

		0, // u6
		f7,
		f8,
		0, // s9
		0, // s10
		1
	};

	const char __cgodebug_str__9[] = s9;
	const unsigned long long __cgodebug_strlen__9 = sizeof(s9)-1;
	const char __cgodebug_str__10[] = s10;
	const unsigned long long __cgodebug_strlen__10 = sizeof(s10)-1;

and again invokes the system C compiler, to produce an object file
containing debug information. Cgo parses the DWARF debug information

a <a href="#Constant_expressions">constant expression</a>,
a <a href="#Conversions">conversion</a> with a result that is a constant, or
the result value of some built-in functions such as
<code>unsafe.Sizeof</code> applied to any value,
<code>cap</code> or <code>len</code> applied to
<a href="#Length_and_capacity">some expressions</a>,
<code>real</code> and <code>imag</code> applied to a complex constant