code: def parse_pipeline(self, batch_size=16):
      memory_allocations: Mapping[int, Optional[Allocation]] = {}
      operation_blocs = []
  
      current_state: State = self.original_state
      current_block = []
  
      # We read the content of the pipeline, validate and collect
      # Memory allocations
      for op_id, operation in enumerate(self.operations):
          previous_state = current_state
          (
              current_state,
              memory_allocation,
          ) = operation.declare_state_and_memory(current_state)
  
          if current_state.jit_mode != previous_state.jit_mode:
              if current_block:
                  operation_blocs.append(
                      (previous_state.jit_mode, current_block)
                  )
              current_block = [op_id]
          else:
              current_block.append(op_id)
  
          memory_allocations[op_id] = memory_allocation
  
      if current_block:
          operation_blocs.append(
              (current_state.jit_mode, current_block)
          )
  
      return operation_blocs, memory_allocations
  
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 2 for calling the Pipeline obj's underlying member method code with example usage: obj.parse_pipeline(batch_size) and returns: operation_blocs,  memory_allocations