<{compressed_data = dense<[1.000000e+00, 2.000000e+00, 5.000000e-01, 2.500000e-01, -1.000000e+00, -2.000000e+00, -5.000000e-01, -2.500000e-01]> : tensor<8xf32>, s_param = #tfl.sparsity_parameter<traversal_order = [0, 1, 2, 3], block_map = [3, 1], dim_metadata = <format = DENSE, dense_size = 16, segments = [], indices = []>, <format = DENSE, dense_size = 1, segments = [], indices = []>, <format = DENSE, dense_size = 1, segments = [], indices = []>, <format = SPARSE_CSR, dense_size = 0, segments = [0,...

<{compressed_data = dense<[1.000000e+00, 2.000000e+00, 5.000000e-01, 2.500000e-01, -1.000000e+00, -2.000000e+00, -5.000000e-01, -2.500000e-01]> : tensor<8xf32>, s_param = #tfl.sparsity_parameter<traversal_order = [0, 1, 2, 3], block_map = [3, 1], dim_metadata = <format = DENSE, dense_size = 16, segments = [], indices = []>, <format = DENSE, dense_size = 1, segments = [], indices = []>, <format = DENSE, dense_size = 1, segments = [], indices = []>, <format = SPARSE_CSR, dense_size = 0, segments = [0,...

  const auto& compressed_data = format_converter.GetData();
  const int dim_size = metadata.size() / 2;
  std::vector<DimensionMetadataAttr> dim_metadata(traversal_order.size());
  for (int i = 0; i < dim_size; i++) {
    if (format[i] == kTfLiteDimDense) {
      dim_metadata[i] = DimensionMetadataAttr::get(
          builder->getContext(),
          ::mlir::TFL::DimensionTypeAttr::get(

  const int dim_metadata_size = tensor.sparsity->dim_metadata.size();
  std::vector<mlir::TFL::DimensionMetadataAttr> dim_metadata(dim_metadata_size);
  for (int i = 0; i < dim_metadata_size; i++) {
    if (tensor.sparsity->dim_metadata[i]->format ==
        tflite::DimensionType_DENSE) {
      dim_metadata[i] = tfl::DimensionMetadataAttr::get(
          builder.getContext(),

  let mnemonic = "sparsity_parameter";
  let parameters = (ins
      I32ArrayParameter:$traversal_order,
      I32ArrayParameter:$block_map,
      ArrayRefParameter<"DimensionMetadataAttr">:$dim_metadata
  );
  let summary = "Sparsity parameter.";
  let assemblyFormat = "`<` struct(params) `>`";
}

def TFL_ConstBytesAttr : AttrDef<TFL_Dialect, "ConstBytes"> {

  // In the traversal order defined above, the metadata needed for
  // each dimension to locate the non-zero values in the original dense tensor.
  // The size of the dim_metadata array = the size of the traversal_order array
  // = n + k.
  dim_metadata:[DimensionMetadata];
}

table Tensor {
  // The tensor shape. The meaning of each entry is operator-specific but

  // In the traversal order defined above, the metadata needed for
  // each dimension to locate the non-zero values in the original dense tensor.
  // The size of the dim_metadata array = the size of the traversal_order array
  // = n + k.
  dim_metadata:[DimensionMetadata];
}

table Tensor {
  // The tensor shape. The meaning of each entry is operator-specific but

  // In the traversal order defined above, the metadata needed for
  // each dimension to locate the non-zero values in the original dense tensor.
  // The size of the dim_metadata array = the size of the traversal_order array
  // = n + k.
  dim_metadata:[DimensionMetadata];
}

// The nested tensor type for VARIANT type.
table VariantSubType {
  // The tensor shape.
  shape:[int];
  type:TensorType;

  for (int i = 0; i < dim_size; i++) {
    const auto dim_metadata = mlir::dyn_cast<mlir::TFL::DimensionMetadataAttr>(
        s_attr.getDimMetadata()[i]);
    if (dim_metadata.getFormat().getValue() ==
        mlir::TFL::DimensionType::DENSE) {
      fb_dim_metadata[i] = tflite::CreateDimensionMetadata(
          builder_, tflite::DimensionType_DENSE, dim_metadata.getDenseSize());

    } else {

      : traversal_order(o.traversal_order),
        block_map(o.block_map) {
  dim_metadata.reserve(o.dim_metadata.size());
  for (const auto &dim_metadata_ : o.dim_metadata) { dim_metadata.emplace_back((dim_metadata_) ? new tflite::DimensionMetadataT(*dim_metadata_) : nullptr); }
}

inline SparsityParametersT &SparsityParametersT::operator=(SparsityParametersT o) FLATBUFFERS_NOEXCEPT {