[DOC] update docstring for hetero map

learnware/market/heterogeneous/organizer/hetero_map/__init__.py

@@ -24,23 +24,6 @@ class HeteroMap(nn.Module):
       is modified for feature extraction purposes only.
 
     The class implements a neural network module for processing tabular data, specifically tuned for numerical features.
-
-    Args:
-        feature_tokenizer (FeatureTokenizer, optional): Tokenizer for feature representation.
-        hidden_dim (int, optional): Dimension of hidden layer.
-        num_layer (int, optional): Number of layers in the transformer encoder.
-        num_attention_head (int, optional): Number of attention heads in the transformer.
-        hidden_dropout_prob (float, optional): Dropout probability for hidden layers.
-        ffn_dim (int, optional): Dimension of feedforward network.
-        projection_dim (int, optional): Dimension for projection head.
-        overlap_ratio (float, optional): Overlap ratio for tokenization.
-        num_partition (int, optional): Number of partitions for collation.
-        temperature (float, optional): Temperature parameter for contrastive learning.
-        base_temperature (float, optional): Base temperature parameter.
-        activation (str, optional): Activation function for transformer layers.
-        device (str, optional): Device to run the model on.
-        checkpoint (str, optional): Path to a pre-trained model checkpoint.
-        **kwargs: Additional keyword arguments.
     """
 
     def __init__(
@@ -58,10 +41,41 @@ class HeteroMap(nn.Module):
         base_temperature=10,
         activation="relu",
         device="cuda:0",
-        checkpoint=None,
         cache_dir=None,
         **kwargs,
     ):
+        """
+        Parameters
+        ----------
+        feature_tokenizer : FeatureTokenizer, optional
+            Tokenizer for feature representation, by default None
+        hidden_dim : int, optional
+            Dimension of hidden layer, by default 128
+        num_layer : int, optional
+            Number of layers in the transformer encoder, by default 2
+        num_attention_head : int, optional
+            Number of attention heads in the transformer, by default 8
+        hidden_dropout_prob : int, optional
+            Dropout probability for hidden layers, by default 0
+        ffn_dim : int, optional
+            Dimension of feedforward network, by default 256
+        projection_dim : int, optional
+            Dimension for projection head, by default 128
+        overlap_ratio : float, optional
+            Overlap ratio for tokenizatio, by default 0.5
+        num_partition : int, optional
+            Number of partitions for collatio, by default 3
+        temperature : int, optional
+            Temperature parameter for contrastive learnin, by default 10
+        base_temperature : int, optional
+            Base temperature paramete, by default 10
+        activation : str, optional
+            Activation function for transformer layer, by default "relu"
+        device : str, optional
+            Device to run the model on, by default "cuda:0"
+        cache_dir : str, optional
+            The cache directory, by default None
+        """
         super(HeteroMap, self).__init__()
 
         self.model_args = {
@@ -74,7 +88,7 @@ class HeteroMap(nn.Module):
             "ffn_dim": ffn_dim,
             "projection_dim": projection_dim,
             "activation": activation,
-            "cache_dir": cache_dir
+            "cache_dir": cache_dir,
         }
         self.model_args.update(kwargs)
 
@@ -139,10 +153,7 @@ class HeteroMap(nn.Module):
             the directory path to save.
         """
         # save model weight state dict
-        model_info = {
-            "model_state_dict": self.state_dict(),
-            "model_args": self.model_args
-        }
+        model_info = {"model_state_dict": self.state_dict(), "model_args": self.model_args}
         torch.save(model_info, checkpoint)
 
     def forward(self, x, y=None):
@@ -361,14 +372,16 @@ class TransformerLayer(nn.Module):
     def forward(self, src, src_mask=None, src_key_padding_mask=None, is_causal=None, **kwargs) -> Tensor:
         """Pass the input through the encoder layer.
 
-        Args:
-            src: the sequence to the encoder layer (required).
-            src_mask: the mask for the src sequence (optional).
-            src_key_padding_mask: the mask for the src keys per batch (optional).
-
-        Shape:
-            see the docs in Transformer class.
+        Parameters
+        ----------
+        src : Any
+            The sequence to the encoder layer.
+        src_mask : Any, optional
+            The mask for the src sequence, by default None
+        src_key_padding_mask : Any, optional
+            The mask for the src keys per batch, by default None
         """
+
         # see Fig. 1 of https://arxiv.org/pdf/2002.04745v1.pdf
         x = src
         if self.use_layer_norm:
@@ -427,8 +440,11 @@ class TransformerMultiLayer(nn.Module):
             self.transformer_encoder.append(stacked_transformer)
 
     def forward(self, embedding, attention_mask=None, **kwargs) -> Tensor:
-        """args:
-        embedding: bs, num_token, hidden_dim
+        """
+        Parameters
+        ----------
+        embedding : Any
+            bs, num_token, hidden_dim
         """
         outputs = embedding
         for i, mod in enumerate(self.transformer_encoder):