class_docstr: Mixup for images. Operates on raw arrays (not tensors).
  
  Parameters
  ----------
  alpha : float
      Mixup parameter alpha
  same_lambda : bool
      Whether to use the same value of lambda across the whole batch, or an
      individually sampled lambda per image in the batch
class_docstr_tok: ["Mixup","for","images",".","Operates","on","raw","arrays","(","not","tensors",")",".","Parameters","--","--","--","--","--","alpha",":","float","Mixup","parameter","alpha","same_lambda",":","bool","Whether","to","use","the","same","value","of","lambda","across","the","whole","batch",",","or","an","individually","sampled","lambda","per","image","in","the","batch"]
code: def declare_state_and_memory(
      self, previous_state: State
  ) -> Tuple[State, Optional[AllocationQuery]]:
      return (
          previous_state,
          AllocationQuery(
              shape=previous_state.shape, dtype=previous_state.dtype
          ),
      )
  
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 ImageMixup obj's underlying member method code with example usage: obj.declare_state_and_memory(previous_state) and returns: previous_state,  AllocationQuery