text: Title: Processes stopped when passing large objects to function to be parallelized
  Body: Problem:
  
  Apply a NLP Deep Learning model for Text Geneartion over the rows of a Pandas Series. The function call is:
  
  `out = text_column.parallel_apply(lambda x: generate_text(args, model, tokenizer, x))`
  
  where `args`, `tokenizer` are light objects but `model` is a heavy object, storing a Pytorch model which weighs more than 6GB on secondary memory and takes up **~12GB RAM** when running it.
  
  I have been doing some tests and the problem arises only when I pass the heavy model to the function (even without effectively running it inside the function), so it seems that the problem is **passing an object as argument that takes up a lot of memory.** (Maybe related with the Sharing Memory strategy for parallel computing.)
  
  After running the `parallel_apply `the output I get is:
  
  ```
  INFO: Pandarallel will run on 8 workers.
  INFO: Pandarallel will use standard multiprocessing data tranfer (pipe) to transfer data between the main process and workers.
     0.00%                                          |        0 /      552 |
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     0.00%                                          |        0 /      551 |
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     0.00%                                          |        0 /      551 |
     0.00%                                          |        0 /      551 |
     0.00%                                          |        0 /      551 |
     0.00%                                          |        0 /      551 |
  ```
  
  And it gets stuck there forever. Indeed, there are two processed spawned and both are **stopped**:
  
  ```
  ablanco+  85448  0.0  4.9 17900532 12936684 pts/27 Sl 14:41   0:00 python3 text_generation.py --input_file input.csv --model_type gpt2  --output_file out.csv --no_cuda --n_cpu 8
  ablanco+  85229 21.4 21.6 61774336 57023740 pts/27 Sl 14:39   2:26 python3 text_generation.py --input_file input.csv --model_type gpt2  --output_file out.csv --no_cuda --n_cpu 8
  ```
  
  