My current architecture. it is designed for time series forecasting. 
I want to implement the following RL "TimeRepresentationAgent" into the model to enable to create an internal representation (a guess) of what hour/day  the hourly time series value correspends to when a label is missing :
# TimeRepresentationAgent (NEWLY ADDED)
class TimeRepresentationAgent(nn.Module):
    def __init__(self, hidden_size, num_time_slots=24):
        super(TimeRepresentationAgent, self).__init__()
        self.hidden_size = hidden_size
        self.num_time_slots = num_time_slots  # e.g., 24 hours in a day
        self.policy_net = nn.Sequential(
            nn.Linear(hidden_size, hidden_size),
            nn.ReLU(),
            nn.Linear(hidden_size, num_time_slots)
        )
        self.time_embedding = nn.Embedding(num_time_slots, hidden_size)

    def forward(self, state):
        # State: (batch_size, hidden_size)
        logits = self.policy_net(state)  # (batch_size, num_time_slots)
        probs = torch.softmax(logits, dim=-1)  # (batch_size, num_time_slots)
        # Compute expected embedding
        time_embed = torch.matmul(probs, self.time_embedding.weight)  # (batch_size, hidden_size)
        return time_embed  # Return the computed embedding  
#decoder class
import torch
import torch.nn as nn
import numpy as np

# Chomp1d remains unchanged
class Chomp1d(nn.Module):
    def __init__(self, chomp_size):
        super(Chomp1d, self).__init__()
        self.chomp_size = chomp_size
        
    def forward(self, x):
        if self.chomp_size > 0:
            return x[:, :, :-self.chomp_size]
        else:
            return x

# Residual Block remains unchanged
class ResidualBlock(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, dilation, dropout):
        super(ResidualBlock, self).__init__()
        padding = (kernel_size - 1) * dilation

        self.conv1 = nn.utils.weight_norm(
            nn.Conv1d(in_channels, out_channels, kernel_size,
                      padding=padding, dilation=dilation)
        )
        self.chomp1 = Chomp1d(padding)
        self.relu1 = nn.ReLU()
        self.dropout1 = nn.Dropout(dropout)
        
        self.conv2 = nn.utils.weight_norm(
            nn.Conv1d(out_channels, out_channels, kernel_size,
                      padding=padding, dilation=dilation)
        )
        self.chomp2 = Chomp1d(padding)
        self.relu2 = nn.ReLU()
        self.dropout2 = nn.Dropout(dropout)

        # Adjust with a 1x1 convolution if in_channels != out_channels
        self.downsample = nn.Conv1d(in_channels, out_channels, kernel_size=1) \
                          if in_channels != out_channels else None

        self.relu = nn.ReLU()
        self.init_weights()
        
    def init_weights(self):
        nn.init.normal_(self.conv1.weight, std=0.01)
        nn.init.normal_(self.conv2.weight, std=0.01)
        if self.downsample is not None:
            nn.init.normal_(self.downsample.weight, std=0.01)
            
    def forward(self, x):
        residual = x  # Save the input for the residual connection
        out = self.conv1(x)
        out = self.chomp1(out)  # Remove extra padding
        out = self.relu1(out)
        out = self.dropout1(out)
        out = self.conv2(out)
        out = self.chomp2(out)  # Remove extra padding
        out = self.relu2(out)
        out = self.dropout2(out)
        if self.downsample is not None:
            residual = self.downsample(residual)
        return self.relu(out + residual)  # Residual connection with addition

class TCNEncoder(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, kernel_size, dropout):
        super(TCNEncoder, self).__init__()
        layers = []
        in_channels = input_size

        for i in range(num_layers):
            dilation = 2 ** i
            out_channels = hidden_size
            layers.append(ResidualBlock(in_channels, out_channels, kernel_size, dilation, dropout))
            in_channels = out_channels  # For next layer

        self.network = nn.Sequential(*layers)

    def forward(self, x):
        # x shape: (batch_size, seq_length, input_size)
        x = x.transpose(1, 2)  # Change to (batch_size, input_size, seq_length)
        x = self.network(x)
        x = x.transpose(1, 2)  # Back to (batch_size, seq_length, hidden_size)
        return x

class TransformerDecoder(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, num_heads, dropout):
        super(TransformerDecoder, self).__init__()
        self.embedding = nn.Linear(input_size, hidden_size)
        self.positional_encoding = PositionalEncoding(hidden_size, dropout)
        decoder_layer = nn.TransformerDecoderLayer(d_model=hidden_size, nhead=num_heads, dropout=dropout)
        self.transformer_decoder = nn.TransformerDecoder(decoder_layer, num_layers=num_layers)
        # self.fc_out = nn.Linear(hidden_size, input_size)  # Not needed here

    def forward(self, tgt, memory, tgt_mask):
        # tgt: (batch_size, tgt_seq_length, input_size)
        tgt_emb = self.embedding(tgt)
        tgt_emb = self.positional_encoding(tgt_emb)
        memory = memory.transpose(0, 1)  # (src_seq_length, batch_size, hidden_size)
        tgt_emb = tgt_emb.transpose(0, 1)  # (tgt_seq_length, batch_size, hidden_size)
        output = self.transformer_decoder(tgt_emb, memory, tgt_mask=tgt_mask)
        output = output.transpose(0, 1)  # Back to (batch_size, tgt_seq_length, hidden_size)
        return output

#parallel clustering model
class TCNEncoderTransformerDecoderEnsemble(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, kernel_size,
                 num_heads, dropout, feature_groups):
        super(TCNEncoderTransformerDecoderEnsemble, self).__init__()

        self.input_size = input_size
        self.hidden_size = hidden_size
        self.feature_groups = feature_groups
        self.num_feature_groups = len(feature_groups)

        # Create sub-models for each feature group
        self.encoders = nn.ModuleList()
        self.decoders = nn.ModuleList()
        self.attention_layers = nn.ModuleList()
        self.final_fc_layers = nn.ModuleList()

        # Shared positional encoding
        self.positional_encoding = PositionalEncoding(hidden_size, dropout)
        self.dropout = nn.Dropout(dropout)

        # Separate alpha parameters for each feature group
        self.alphas = nn.ParameterList([nn.Parameter(torch.full((hidden_size,), 0.5)) for _ in range(self.num_feature_groups)])

        for i, group_indices in enumerate(self.feature_groups):
            current_input_size = len(group_indices)
            if current_input_size > 0:
                encoder = TCNEncoder(current_input_size, hidden_size, num_layers, kernel_size, dropout)
                decoder = TransformerDecoder(current_input_size, hidden_size, num_layers, num_heads, dropout)
                attention_layer = nn.MultiheadAttention(embed_dim=hidden_size, num_heads=num_heads, dropout=dropout)
                final_fc_layer = nn.Linear(hidden_size, current_input_size)

                self.encoders.append(encoder)
                self.decoders.append(decoder)
                self.attention_layers.append(attention_layer)
                self.final_fc_layers.append(final_fc_layer)

    def forward(self, src, tgt):
        # src: (batch_size, src_seq_length, input_size)
        # tgt: (batch_size, tgt_seq_length, input_size)

        batch_size = src.size(0)
        src_seq_length = src.size(1)
        tgt_seq_length = tgt.size(1)
        input_size = src.size(2)

        final_outputs = [torch.zeros(batch_size, tgt_seq_length, input_size).to(src.device) for _ in range(self.num_feature_groups)]

        for i, group_indices in enumerate(self.feature_groups):
            if len(group_indices) > 0:
                # Extract feature subsets based on dynamic grouping
                src_sub = src[:, :, group_indices]
                tgt_sub = tgt[:, :, group_indices]

                # Get the corresponding sub-models
                encoder = self.encoders[i]
                decoder = self.decoders[i]
                attention_layer = self.attention_layers[i]
                final_fc_layer = self.final_fc_layers[i]
                alpha = torch.sigmoid(self.alphas[i]).to(src.device).view(1, 1, -1)

                # Encoder
                memory = encoder(src_sub)
                memory = self.positional_encoding(memory)

                # Decoder
                tgt_mask = nn.Transformer.generate_square_subsequent_mask(tgt_sub.size(1)).to(tgt_sub.device)
                decoder_output = decoder(tgt_sub, memory, tgt_mask)

                # Reshape for MultiheadAttention
                decoder_output_t = decoder_output.transpose(0, 1)
                memory_t = memory.transpose(0, 1)

                # Additional Attention Layer
                attn_output, _ = attention_layer(decoder_output_t, memory_t, memory_t)
                attn_output = attn_output.transpose(0, 1)

                # Fusion Strategy: Weighted Sum
                combined = alpha * decoder_output + (1 - alpha) * attn_output
                combined = self.dropout(combined)

                # Final Output Layer for the subset
                final_output_sub = final_fc_layer(combined)

                # Place the output of the sub-model into the correct feature columns of the final output tensor
                for j, original_feature_index in enumerate(group_indices):
                    final_outputs[i][:, :, original_feature_index] = final_output_sub[:, :, j]

        # Sum the outputs of the sub-models
        final_output = torch.stack(final_outputs).sum(dim=0)

        return final_output

def group_features_clustering(df, n_clusters=None, linkage='ward'):
    """
    Groups features using clustering on their time series profiles.

    Args:
        df (pd.DataFrame): Input DataFrame.
        n_clusters (int, optional): Number of clusters to form. Defaults to None, which will result in the algorithm finding the optimal number of clusters.
        linkage (str): Which linkage criterion to use. The linkage criterion determines which distance to use between sets of observation. The algorithm will merge the pairs of cluster that minimize this criterion.

    Returns:
        list of list: A list where each sublist contains the indices of grouped features.
    """
    scaler = StandardScaler()
    df_scaled = pd.DataFrame(scaler.fit_transform(df.T).T) # Scale features

    if n_clusters is None:
        # Silhouette analysis to find optimal number of clusters (can be computationally intensive for large number of features)
        from sklearn.metrics import silhouette_score
        range_n_clusters = range(2, min(11, df_scaled.shape[1]))  # Adjust range as needed
        silhouette_scores = []
        for n_clust in range_n_clusters:
            clusterer = AgglomerativeClustering(n_clusters=n_clust, linkage=linkage)
            cluster_labels = clusterer.fit_predict(df_scaled.T)
            silhouette_avg = silhouette_score(df_scaled.T, cluster_labels)
            silhouette_scores.append(silhouette_avg)
        optimal_n_clusters = range_n_clusters[np.argmax(silhouette_scores)]
        clusterer = AgglomerativeClustering(n_clusters=optimal_n_clusters, linkage=linkage)
    else:
        clusterer = AgglomerativeClustering(n_clusters=n_clusters, linkage=linkage)

    cluster_labels = clusterer.fit_predict(df_scaled.T)
    feature_clusters = {i: [] for i in range(clusterer.n_clusters)}
    for idx, label in enumerate(cluster_labels):
        feature_clusters[label].append(idx) # Use index instead of column name

    return list(feature_clusters.values())