class_docstr: Transform using the given torch.nn.Module
  
  Parameters
  ----------
  module: torch.nn.Module
      The module for transformation
class_docstr_tok: ["Transform","using","the","given","torch.nn.Module","Parameters","--","--","--","--","--","module",":","torch.nn.Module","The","module","for","transformation"]
code: def declare_state_and_memory(
      self, previous_state: State
  ) -> Tuple[State, Optional[AllocationQuery]]:
      return previous_state, None
  
file_path: ffcv/ffcv/transforms/module.py
imports: import torch
  from numpy.random import permutation, rand
  from typing import Callable, Optional, Tuple
  from ..pipeline.allocation_query import AllocationQuery
  from ..pipeline.operation import Operation
  from ..pipeline.state import State
label_desc: Use image node_id 3 for calling the ModuleWrapper obj's underlying member method code with example usage: obj.declare_state_and_memory(previous_state) and returns: previous_state,  None