code: def recv(cls, comm, out_array, peer, stream=None):
      shape_and_sizes = cls._exchange_shape_and_sizes(
          comm, peer, (), "recv", stream
      )
      # Change the array sizes in out_array to match the sent ones
      # Receive the three arrays
      # TODO(ecastill) dtype is not correct, it must match the internal
      # sparse matrix arrays dtype
      arrays = cls._get_internal_arrays(out_array)
      shape = tuple(shape_and_sizes[0:2])
      sizes = shape_and_sizes[2:]
      # TODO(use the out_array datatypes)
      arrs = [
          cupy.empty(s, dtype=a.dtype) for s, a in zip(sizes, arrays)
      ]
      nccl.groupStart()
      for a in arrs:
          cls._recv(comm, a, peer, a.dtype, a.size, stream)
      nccl.groupEnd()
      # Create a sparse matrix from the received arrays
      cls._assign_arrays(out_array, arrs, shape)
  
imports: import numpy
  import warnings
  import cupy
  from cupy.cuda import nccl
  from cupyx.distributed import _store
  from cupyx.distributed._comm import _Backend
  from cupyx.scipy import sparse
label_desc: Use image node_id 12 for calling the _SparseNCCLCommunicator obj's underlying member method code with example usage: obj.recv(cls, comm, out_array, peer, stream) without return types