// input split is omitted, default to split by 1.
            const auto* const attr_proto = ctx.getAttribute("keepdims");
            if (attr_proto) {
              keepdims = attr_proto->i();
            }
          } else {
            splitSize = [&]() -> int64_t {
              // Need input split shape info and initializer data to infer split sizes.
              if (!hasInputShape(ctx, 1)) {
                return -1;
              }
              const TensorProto* splitInitializer = ctx.getInputData(1);
              if (nullptr == splitInitializer || !splitInitializer->has_data_type()) {
                return -1;
              }

              std::vector<int64_t> splitSizes;
              if (splitInitializer->data_type() == TensorProto::INT64) {
                const auto data = ParseData<int64_t>(splitInitializer);
                splitSizes.insert(splitSizes.end(), data.begin(), data.end());
              } else if (splitInitializer->data_type() == TensorProto::INT32) {
                const auto data = ParseData<int32_t>(splitInitializer);
                splitSizes.insert(splitSizes.end(), data.begin(), data.end());
              } else {
                // unaccepted data type
                fail_shape_inference("Only supports `int32_t` or `int64_t` inputs for split");
              }

              if (splitSizes.empty()) {
                fail_shape_inference("Input 'split' can not be empty.");
              }

              const auto& splitDim = inputShape.dim(axis);
              if (!splitDim.has_dim_value()) {
                // Unable to verify nor infer exact split dimension size.
                return -1;
              }

              int64_t splitDimValue = splitDim.dim_value();
              const auto& splitShape = getInputShape(ctx, 1);
              if (splitShape.dim_size() == 0) {
                // split is scalar
                if (splitDimValue % splitSizes[0] == 0) {
                  // all output chunks have the same shape, assign that to output sequence shape.
                  return splitSizes[0];
                }
                return -1;
              } else {
                // split is 1-D tensor
                int64_t splitSizesSum = std::accumulate(splitSizes.begin(), splitSizes.end(), static_cast<int64_t>(0));
                if (splitDimValue != splitSizesSum) {
                  fail_shape_inference(
                      "Sum of split values not equal to 'input' dim size on 'axis'. 'axis' dim size=",
                      splitDimValue,
                      " sum of split values=",
                      splitSizesSum);
                }
                if (std::adjacent_find(splitSizes.begin(), splitSizes.end(), std::not_equal_to()) == splitSizes.end()) {
                  // all split sizes are the same.
                  return splitSizes[0];
                }