code: def get_param_baseline():
      params = {
          "lr": 0.1,  # learning rate [0.1]
          "dim": 100,  # size of word vectors [100]
          "ws": 5,  # size of the context window [5]
          "epoch": 50,  # number of epochs [5]
          "minCount": 1,  # minimal number of word occurrences [1]
          "minCountLabel": 1,  # minimal number of label occurrences [1]
          "minn": 2,  # min length of char ngram [0]
          "maxn": 6,  # max length of char ngram [0]
          "neg": 5,  # number of negatives sampled [5]
          "wordNgrams": 3,  # max length of word ngram [1]
          "loss": "softmax",  # loss function {ns, hs, softmax, ova} [softmax]
          "bucket": 2000000,  # number of buckets [2000000]
          # thread: number of threads [number of cpus]
          "lrUpdateRate": 100,  # change the rate of updates for the learning rate [100]
          "t": 0.0001,  # sampling threshold [0.0001]
          # label: prefix ['__label__']
          # pretrainedVectors:  pretrained word vectors (.vec file) for supervised learning []
          # "verbose": 2,
          "quantize_model": True,
      }
      return params.copy()
  
label_desc: Use image node_id 1 for calling a global function with example usage: get_param_baseline() and returns: params