我正在尝试通过此链接https://docs.python.org/3/library/multiprocessing.shared_memory.html

# In the first Python interactive shell
import numpy as np
a = np.array([1,1,2,3,5,8])  # Start with an existing NumPy array
from multiprocessing import shared_memory
shm = shared_memory.SharedMemory(create=True,size=a.nbytes)
# Now create a NumPy array backed by shared memory
b = np.ndarray(a.shape,dtype=a.dtype,buffer=shm.buf)
b[:] = a[:]  # Copy the original data into shared memory
shname = shm.name  # We did not specify a name so one was chosen for us
print(shname)
print(a)
print(b)

# In either the same shell or a new Python shell on the same machine
import numpy as np
from multiprocessing import shared_memory
# Attach to the existing shared memory block
existing_shm = shared_memory.SharedMemory(name=shname)
# Note that a.shape is (6,) and a.dtype is np.int64 in this example
c = np.ndarray((6,),dtype=np.int64,buffer=existing_shm.buf)
print(c)
c[-1] = 888
print(c)
# Back in the first Python interactive shell,b reflects this change

# Clean up from within the second Python shell
del c  # Unnecessary; merely emphasizing the array is no longer used
existing_shm.close()

# Clean up from within the first Python shell
del b  # Unnecessary; merely emphasizing the array is no longer used
shm.close()
shm.unlink()  # Free and release the shared memory block at the very end

在示例中，c的输出应为array([1,8])，但是运行此命令时，我得到：

wnsm_26020d1b
[1 1 2 3 5 8]
[1 1 2 3 5 8]
[ 4294967297 12884901890 34359738373           0           0           0]
[ 4294967297 12884901890 34359738373           0           0         888]

我完全错过了什么吗？其他人有这个结果吗？