int64_t dims[] = {2, 2};
  double data[] = {1.0, 2.0, 3.0, 4.0};
  TF_Status* status = TF_NewStatus();
  TF_Tensor* t = TFE_AllocateHostTensor(ctx, TF_DOUBLE, &dims[0],
                                        sizeof(dims) / sizeof(int64_t), status);
  memcpy(TF_TensorData(t), &data[0], TF_TensorByteSize(t));
  TFE_TensorHandle* th = TFE_NewTensorHandleFromTensor(ctx, t, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

@@ -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);

    [[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 =

      TF_AllocateTensor(TF_INT32, dims, num_dims, sizeof(int32_t) * num_values);
  memcpy(TF_TensorData(t), values, sizeof(int32_t) * num_values);
  return t;
}

TF_Tensor* Int32Tensor(const std::vector<int32_t>& values) {
  int64_t dims = values.size();
  return Int32Tensor(&dims, 1, values.data());
}

TF_Tensor* Int32Tensor(int32_t v) {
  const int num_bytes = sizeof(int32_t);
  int32_t* values = new int32_t[1];

		// 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[:]...)
	}

    float value = 0.0f;
    TF_Tensor* t = TFE_TensorHandleResolve(handle_1, status);
    ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
    ASSERT_EQ(sizeof(float), TF_TensorByteSize(t));
    memcpy(&value, TF_TensorData(t), sizeof(float));
    TF_DeleteTensor(t);
    EXPECT_EQ(1.2f, value);
    TFE_DeleteTensorHandle(handle_1);
    TF_DeleteStatus(status);
    TFE_DeleteTensorHandle(var_handle);
  }

  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);