# ===================================================================
# Configuration for Decoding Novel Latent Embeddings
# ===================================================================

paths:
  # Path to the PRE-TRAINED decoder model checkpoint from the chebnet_conditional_setup.py run.
  decoder_checkpoint_path: "checkpoints/decoder2_checkpoint.pth"

# --- Decoder Model Settings ---
# These parameters MUST EXACTLY MATCH the parameters used to train the decoder
# in the chebnet_conditional_setup.py script (defined in its param_conditional.yaml).
decoder2_settings:
  # The dimensionality of the dynamic per-atom embeddings from the HNO encoder.
  # This corresponds to hno_encoder.hidden_dim in the training config.
  node_emb_dim: 16
  
  # The dimensionality of the static z_ref conditioner embeddings.
  # This also corresponds to hno_encoder.hidden_dim.
  cond_emb_dim: 16
  
  # The dimensions of the 2D pooling layer.
  output_height: 50
  output_width: 2
  
  # The dimensionality of the hidden layers within the final MLP decoder.
  mlp_hidden_dim: 16
  
  # The number of hidden layers in the final MLP decoder.
  num_hidden_layers: 12