EXPECT_TF_META("v", list.size(), TF_ATTR_STRING, list_total_size);
  std::unique_ptr<void*[]> values(new void*[list.size()]);
  std::unique_ptr<size_t[]> lens(new size_t[list.size()]);
  std::unique_ptr<char[]> storage(new char[list_total_size]);
  TF_OperationGetAttrStringList(oper, "v", values.get(), lens.get(),
                                list.size(), storage.get(), list_total_size,
                                s_);

          s, TF_ATTR_STRING, metadata.total_size = 0;
          for (int i = 0; i < attr->list().s_size();
               ++i) { metadata.total_size += attr->list().s(i).size(); });
      LIST_CASE(i, TF_ATTR_INT);
      LIST_CASE(f, TF_ATTR_FLOAT);
      LIST_CASE(b, TF_ATTR_BOOL);
      LIST_CASE(type, TF_ATTR_TYPE);
      LIST_CASE(
          shape, TF_ATTR_SHAPE, metadata.total_size = 0;

          {"SparseSoftmaxCrossEntropyWithLogits", {1}},
          {"FusedBatchNorm", {1, 2, 3, 4}},
      });
  return m;
}

}  // namespace

// If over kMinAggregateCount gradients are accumulated and the total
// memory consumption is over kMinAggregateBytes, do an early aggregation
// so as to release the gradient tensor to save memory.
constexpr int kMinAggregateCount = 4;
constexpr int kMinAggregateBytes = 128 * 1024 * 1024;

    `with_values` function of `RaggedTensor`.
*   Adds `StatelessCase` op, and uses it if none of case branches has stateful
    ops.
*   Adds `tf.config.experimental.get_memory_usage` to return total memory usage
    of the device.
*   Adds gradients for `RaggedTensorToVariant` and `RaggedTensorFromVariant`.
*   Improve shape inference of nested function calls by supporting constant