}
  OpBuilder builder(stack);
  Value buffer;
  if (failed(cutil::CreateInitBufferValue(
          elem_type->getShape(), stack.getMaxSize(), stack,
          elem_type->getElementType(), builder, &buffer))) {
    return failure();
  }
  auto size_var_type = GetSizeVarType(builder);
  auto var_type = RankedTensorType::get(
      {}, TF::ResourceType::get(

  for (auto it : llvm::enumerate(var_access_info.new_operand_values)) {
    Type type = it.value().getType();
    if (type.isa<TensorType>() &&
        type.cast<TensorType>().getElementType().isa<TF::ResourceType>()) {
      if (!llvm::is_contained(device_var_reads_indices, it.index()) &&
          !llvm::is_contained(device_var_updates_indices, it.index())) {

  return types;
}

bool IsResourceType(Type val_type) {
  if (auto tensor_type = mlir::dyn_cast<mlir::TensorType>(val_type)) {
    if (mlir::isa<TF::ResourceType>(tensor_type.getElementType())) {
      return true;
    }
  }
  return false;
}

bool IsTPUOp(mlir::Operation* op) {
  return op->hasAttr(TF::kReplicationInfoAttr);
}

      if (dims[dim] == mlir::ShapedType::kDynamic) {
        dims[dim] = encoding.getBounds()[dim];
      }
    }
  }
  return GetTypeFromTFTensorShape(dims, ranked_ty.getElementType());
}

// Calculates computation output shape and build OutputDescription for each
// output based on static shapes in MLIR module. If an output is a resource