code: def get_fixed_params():
      """Parameters that currently cannot be searched during HPO"""
      fixed_params = {
          "batch_size": 512,  # The size of example chunks to predict on.
          "n_cont_embeddings": 0,  # How many continuous feature embeddings to use.
          "norm_class_name": "LayerNorm",  # What kind of normalization to use on continuous features.
          "column_embedding": True,  # If True, 1/(n_shared_embs)th of every embedding will be reserved for a learned parameter that's common to all embeddings.
          #'shared_embedding': False,
          #'n_shared_embs': 8,
          "orig_emb_resid": False,  # If True, concatenate the original embeddings on top of the feature embeddings in the Transformer layers.
          "one_hot_embeddings": False,  # If True, one-hot encode variables whose cardinality is < max_emb_dim.
          "drop_whole_embeddings": False,  # If True, dropout pretends the embedding was a missing value. If false, dropout sets embed features to 0
          "max_emb_dim": 8,  # Maximum allowable amount of embeddings.
          "base_exp_decay": 0.95,  # Rate of exponential decay for learning rate, used during finetuning.
          "encoders": {
              "CATEGORICAL": "CategoricalOrdinalEnc",  # How to "encode"(vectorize) each column type.
              "DATETIME": "DatetimeOrdinalEnc",
              "LATLONG": "LatLongQuantileOrdinalEnc",
              "SCALAR": "ScalarQuantileOrdinalEnc",
              "TEXT": "TextSummaryScalarEnc",
          },
          "aug_mask_prob": 0.4,  # What percentage of values to apply augmentation to.
          "num_augs": 0,  # Number of augmentations to add.
          "pretext": "BERTPretext",  # What pretext to use when performing pretraining/semi-supervised learning.
          "n_cont_features": 8,  # How many continuous features to concatenate onto the categorical features
          "fix_attention": False,  # If True, use the categorical embeddings in the transformer architecture.
          "epochs": 200,  # How many epochs to train on with labeled data.
          "pretrain_epochs": 200,  # How many epochs to pretrain on with unlabeled data.
          "epochs_wo_improve": 30,  # How many epochs to continue running without improving on metric. aka "Early Stopping Patience"
          "num_workers": 16,  # How many workers to use for torch DataLoader.
          "max_columns": 500,  # Maximum number of columns TabTransformer will accept as input. This is to combat huge memory requirements/errors.
          "tab_readout": "none",  # What sort of readout from the transformer. Options: ['readout_emb', 'mean', 'concat_pool', 'concat_pool_all', 'concat_pool_add', 'all_feat_embs', 'mean_feat_embs', 'none']
      }
  
      return fixed_params
  
label_desc: Use image node_id 1 for calling a global function with example usage: get_fixed_params() and returns: fixed_params