if (value.IsBool()) {
      mlir_vector.push_back(BuildVhloBooleanV1Attr(value.AsBool(), builder));
    } else if (value.IsString()) {
      mlir_vector.push_back(
          BuildVhloStringV1Attr(value.AsString().str(), builder));
    } else if (value.IsInt()) {
      mlir_vector.push_back(BuildVhloIntV1Attr(value.AsInt64(), builder));
    } else if (value.IsFloat()) {
      mlir_vector.push_back(BuildVhloFloatV1Attr(value.AsFloat(), builder));

      .def("front", &mlir::Region::front, py::return_value_policy::reference)
      .def("add_block", [](mlir::Region& r) { r.push_back(new mlir::Block); })
      .def("push_back", &mlir::Region::push_back)
      .def("size", [](mlir::Region& r) { return r.getBlocks().size(); })
      .def("front", &mlir::Region::front, py::return_value_policy::reference);

        remaining_incoming_ctrl_edges[successor]--;
        if (remaining_incoming_ctrl_edges[successor] == 0 &&
            remaining_incoming_data_edges[successor] == 0) {
          ready.push_back(successor);
        }
      }
    }
    result.push_back(best);
  }
  return result;
}

}  // namespace TF

    input_arguments.push_back(tensor_i32);
    input_arguments.push_back(one_matrix->getResult(0));
    lhs_rank = tensor_shape.size();
    rhs_rank = opponent_shape.size();
  } else {
    output_shape =
        CreateOutputShape(reduce_dnums, opponent_shape, tensor_shape);
    input_arguments.push_back(one_matrix->getResult(0));
    input_arguments.push_back(tensor_i32);
    lhs_rank = opponent_shape.size();

#include <optional>

namespace mlir {
namespace TF {

void RuntimeDevices::AddDevice(const ParsedName& device) {
  device_names_.push_back(device);
}

void RuntimeDevices::AddGpuDevice(const ParsedName& device,
                                  const GpuDeviceMetadata& metadata) {
  device_names_.push_back(device);
  gpu_metadata_.insert({DeviceNameUtils::ParsedNameToString(device), metadata});
}

      if (wordStart) {
        result.push_back(delimiter);
      }
      wordStart = true;
      continue;
    }
    if (!from_snake && isupper(c)) {
      wordStart = true;
    }

    // add delimiter
    if (wordStart && toSnake && !inputStart) {
      result.push_back(delimiter);
    }

    // add the next letter from the input string (choosing upper/lower case)

  std::vector<Type> input_types;
  std::vector<Type> output_types;

  input_types.reserve(inputs.size());
  for (Value v : inputs) {
    input_types.push_back(v.getType());
  }
  output_types.reserve(outputs.size());
  for (Value v : outputs) {
    output_types.push_back(v.getType());
  }

  // Create the function.
  MLIRContext* context = ops[0]->getContext();

    std::vector<const Edge*> control_edges;
    std::vector<OutEdgeInfo> data_edges;
    for (auto e : n->out_edges()) {
      if (e->IsControlEdge()) {
        control_edges.push_back(e);
      } else {
        data_edges.push_back({e->dst(), e->src_output(), e->dst_input()});
      }
    }
    for (const Edge* e : control_edges) {
      g->AddControlEdge(original_node, e->dst());
      g->RemoveEdge(e);
    }

      control_fetches.push_back(value);
    } else {
      data_fetches.push_back(value);
      data_types.push_back(value.getType());
    }
  }
  auto island = OpBuilder(fetch).create<IslandOp>(
      fetch.getLoc(), data_types,
      /*control=*/ControlType::get(fetch.getContext()),
      /*controlInputs=*/control_fetches);
  island.getBody().push_back(new Block);

      transpose_ops.push_back(transpose);
    }
  }

  // Nothing to do here.
  if (!permutation_op || transpose_ops.empty()) return;
  SmallVector<int64_t, 4> permutation;
  auto perm_attr = mlir::cast<DenseElementsAttr>(permutation_op.getValue());
  for (const auto& value : perm_attr.getValues<APInt>())
    permutation.push_back(value.getSExtValue());