text: Title: Memory leaks when using doc_topics in LdaSeqModel
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  #### Problem description
  
  I trained a large LdaSeqModel with 53,000 documents, 30 topics, and 18 timeslices.
  I saved the model to disk because it ran for 7 days.
  
  When extracting topic probabilities for 53,000 documents, memory usage rises above 120GB.
  However, only extracting probabilities for 1,000 documents works flawlessly.
  #### Steps/code/corpus to reproduce
  
  1. Train LdaSeqModel
  2. Save LdaSeqModel
  2. Load LdaSeqModel from disk
  4. Extract document-topic probabilities with `doc_topics`
  
  ldaseq.corpus_len = 53,101 in the below MWE.
  
  ```python
  from gensim.models import LdaSeqModel
  
  ldaseq = LdaSeqModel.load("/ldaseq_model")
  
  prob = (ldaseq.doc_topics(x) for x in range(ldaseq.corpus_len)) 
  
  df = pd.DataFrame(prob, columns=[f"topic_{i}" for i in range(30)])
  ```
  
  #### Versions
  
  macOS-10.16-x86_64-i386-64bit
  Python 3.8.8 | packaged by conda-forge | (default, Feb 20 2021, 16:12:38) 
  [Clang 11.0.1 ]
  Bits 64
  NumPy 1.20.1
  SciPy 1.6.1
  gensim 3.8.3
  FAST_VERSION 1