code: def declare_state_and_memory(
      self, previous_state: State
  ) -> Tuple[State, Optional[AllocationQuery]]:
      # Should already be converted to tensor
      assert not previous_state.jit_mode
      return (
          replace(previous_state, shape=(self.num_classes,)),
          AllocationQuery(
              (self.num_classes,),
              dtype=previous_state.dtype,
              device=previous_state.device,
          ),
      )
  
imports: from typing import Tuple
  from numba import objmode
  import numpy
  import torch
  import torch.nn.functional
  from dataclasses import replace
  from typing import Callable, Optional, Tuple
  from ..pipeline.allocation_query import AllocationQuery
  from ..pipeline.operation import Operation
  from ..pipeline.state import State
  from ..pipeline.compiler import Compiler
label_desc: Use image node_id 3 for calling the MixupToOneHot obj's underlying member method code with example usage: obj.declare_state_and_memory(previous_state) and returns: replace,  AllocationQuery