code: def allocate_memory(self, batch_size, batches_ahead):
      memory_buffers = defaultdict(dict)
      full_memory_requirements, _ = self.collect_requirements()
  
      for kind, requirements in full_memory_requirements.items():
          for node_id, memory_allocation in requirements.items():
              # If the operation didn't make a query we stop here
              allocated_buffer = None
              if isinstance(memory_allocation, AllocationQuery):
                  allocated_buffer = allocate_query(
                      memory_allocation, batch_size, batches_ahead
                  )
              elif isinstance(memory_allocation, Sequence):
                  allocated_buffer = tuple(
                      allocate_query(q, batch_size, batches_ahead)
                      for q in memory_allocation
                  )
  
              memory_buffers[kind][node_id] = allocated_buffer
  
      return memory_buffers
  
imports: from distutils.log import warn
  import warnings
  import ast
  from collections import defaultdict
  from typing import Callable, Dict, List, Optional, Sequence, Set
  from abc import ABC, abstractmethod
  from ffcv.pipeline.allocation_query import AllocationQuery
  from ffcv.pipeline.pipeline_spec import PipelineSpec
  from ffcv.pipeline.compiler import Compiler
  from ffcv.pipeline.allocation_query import allocate_query
  from .operation import Operation
  from ..transforms import ModuleWrapper
  from .state import State
  import torch
  import numpy
label_desc: Use image node_id 3 for calling the Graph obj's underlying member method code with example usage: obj.allocate_memory(batch_size, batches_ahead) and returns: memory_buffers