code: def allocate_memory(self, batch_size: int, batches_ahead: int):
      _, memory_allocations = self.parse_pipeline()
      # Contains the actual allocated memory
      memory_buffers: Mapping[int, Any] = {}
  
      # For each allocation made by the operations in the pipeline
      for op_id, memory_allocation in memory_allocations.items():
          # If the operation didn't make a query we stop here
          allocated_buffer = None
          if isinstance(memory_allocation, AllocationQuery):
              allocated_buffer = self.allocate_query(
                  memory_allocation, batch_size, batches_ahead
              )
          elif isinstance(memory_allocation, Sequence):
              allocated_buffer = tuple(
                  self.allocate_query(q, batch_size, batches_ahead)
                  for q in memory_allocation
              )
  
          memory_buffers[op_id] = allocated_buffer
  
      return memory_buffers
  
file_path: ffcv/ffcv/pipeline/pipeline.py
imports: from typing import Any, Optional, Sequence, Mapping
  import torch
  import numpy
  from .state import State
  from .operation import Operation
  from .allocation_query import Allocation, AllocationQuery
label_desc: Use image node_id 5 for calling the Pipeline obj's underlying member method code with example usage: obj.allocate_memory(batch_size, batches_ahead) and returns: memory_buffers