code: def ram_read_write_worker():
      schema = {
          "tf": SizeData(dtype="float32", shape=(112, 112)),
          "ti": SizeData(dtype="int32", shape=(4, 64, 64)),
      }
      storage = SingleProcessRamTensorStorage(schema, io.BytesIO())
      world_size = comm.get_world_size()
      rank = comm.get_rank()
      data_elts = []
      # prepare different number of tensors in different processes
      for i in range(rank + 1):
          data_elt = {
              "tf": torch.ones((112, 112), dtype=torch.float32)
              * (rank + i * world_size),
              "ti": torch.ones((4, 64, 64), dtype=torch.int32)
              * (rank + i * world_size),
          }
          data_elts.append(data_elt)
      # write data to the single process storage
      for i in range(rank + 1):
          record_id = storage.put(data_elts[i])
          assert (
              record_id == i
          ), f"Process {rank}: record ID {record_id}, expected {i}"
      comm.synchronize()
      # gather all data in process rank 0
      multi_storage = storage_gather(storage)
      if rank != 0:
          return
      # read and check data from the multiprocess storage
      for j in range(world_size):
          for i in range(j):
              record = multi_storage.get(j, i)
              record_gt = {
                  "tf": torch.ones((112, 112), dtype=torch.float32)
                  * (j + i * world_size),
                  "ti": torch.ones((4, 64, 64), dtype=torch.int32)
                  * (j + i * world_size),
              }
              assert len(record) == len(schema), (
                  f"Process {rank}: multi storage record, rank {j}, id {i}: "
                  f"expected {len(schema)} fields in the record, got {len(record)}"
              )
              for field_name in schema:
                  assert field_name in record, (
                      f"Process {rank}: multi storage record, rank {j}, id {i}: "
                      f"field {field_name} not in the record"
                  )
  
                  assert (
                      record_gt[field_name].shape
                      == record[field_name].shape
                  ), (
                      f"Process {rank}: multi storage record, rank {j}, id {i}: "
                      f"field {field_name}, expected shape {record_gt[field_name].shape} "
                      f"got {record[field_name].shape}"
                  )
                  assert (
                      record_gt[field_name].dtype
                      == record[field_name].dtype
                  ), (
                      f"Process {rank}: multi storage record, rank {j}, id {i}: "
                      f"field {field_name}, expected dtype {record_gt[field_name].dtype} "
                      f"got {record[field_name].dtype}"
                  )
                  assert torch.allclose(
                      record_gt[field_name], record[field_name]
                  ), (
                      f"Process {rank}: multi storage record, rank {j}, id {i}: "
                      f"field {field_name}, tensors are not close enough:"
                      f"L-inf {(record_gt[field_name]-record[field_name]).abs_().max()} "
                      f"L1 {(record_gt[field_name]-record[field_name]).abs_().sum()} "
                  )
  
imports: import io
  import tempfile
  import unittest
  from contextlib import ExitStack
  import torch
  import torch.distributed
  import torch.multiprocessing
  from detectron2.utils import comm
  from densepose.evaluation.tensor_storage import SingleProcessFileTensorStorage, SingleProcessRamTensorStorage, SizeData, storage_gather
label_desc: Use image node_id 4 for calling a global function with example usage: ram_read_write_worker() without return types