code: def recv_forward(tensor_shapes, config):
      input_tensors = []
      for tensor_shape in tensor_shapes:
          if tensor_shape is None:
              input_tensors.append(None)
          else:
              input_tensors.append(
                  p2p_communication.recv_forward(tensor_shape, config)
              )
      return input_tensors
  
imports: import contextlib
  from typing import Callable, Iterator, List, Optional, Union
  import torch
  from torch.autograd.variable import Variable
  from megatron.core import parallel_state
  from megatron.core.enums import ModelType
  from megatron.core.pipeline_parallel import p2p_communication
  from megatron.core.utils import get_attr_wrapped_model, get_model_config, get_model_type
label_desc: Use image node_id 9 for calling a global function with example usage: recv_forward(tensor_shapes, config) and returns: input_tensors