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 corresponds to when a label is missing in the data so this is supposed to be a model side solution that doesn't require the data to be preprocessed. simply edit the ensemble larger model to incorporate the reinforcement learning TimeRepresentationAgent. attached are all the necessary modules plus the training and validation function so you can incorporate the RL TimeRepresentationAgent into the model and train it

TimeRepresentationAgent 
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

# Function to train the model with teacher forcing
def train_model(model, train_loader, val_loader, num_epochs, learning_rate, gradient_clip_val, train_only = False):
    criterion = nn.MSELoss()
    optimizer = torch.optim.AdamW([
    {'params': model.parameters(), 'weight_decay' : 0.01},
    #{'params': criterion.parameters(), 'weight_decay' : 0.0}
        ], lr=learning_rate)
    processor_type= 'cuda' if torch.cuda.is_available() else 'cpu'
    scaler = GradScaler()
    device = torch.device(processor_type)
    model.to(device)
    
    #scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr=learning_rate*2, steps_per_epoch=len(train_loader), epochs=num_epochs)
    
    # Track best validation loss for early stopping
    best_val_loss = float('inf')
    patience = 10
    patience_counter = 0
    pbar = tqdm(range(num_epochs), desc=f"Epochs", unit="epoch")
    for epoch in pbar:
        model.train()
        train_loss = 0.0
        train_sMAPE = 0.0
        train_MASE = 0.0
        for batch_x, batch_y in train_loader:
            
            batch_x, batch_y = _prepare_batch(batch_x, batch_y, device)
            # Prepare decoder input by shifting targets (teacher forcing)
            decoder_input = _prepare_decoder_input(batch_x, batch_y, True)

            optimizer.zero_grad(set_to_none=True)
            
            with autocast(device_type= processor_type):
                output = model(batch_x, decoder_input)
                loss = criterion(output, batch_y)
                batch_sMAPE = smape(batch_y, output)
                batch_MASE = 0#mase(batch_y, output, torch.tensor(train_data_Weekly.values, dtype=torch.float32).to(device))
            # Scale the loss and call backward
            
            scaler.scale(loss).backward()
            scaler.unscale_(optimizer)
            torch.nn.utils.clip_grad_norm_(model.parameters(), gradient_clip_val)
            scaler.step(optimizer)
            scaler.update()
            train_loss += loss.item()
            train_sMAPE += batch_sMAPE
            train_MASE += batch_MASE
            #scheduler.step()
            
        train_loss /= len(train_loader)
        train_sMAPE /= len(train_loader)
        train_MASE /= len(train_loader)

        if not(train_only):
            Flag, best_val_loss, losses = validate_model(model, val_loader, criterion, best_val_loss, processor_type, patience_counter, patience)
            val_loss, val_sMAPE, val_MASE = losses

            #break if patience runs out
            if not(Flag):
                break
            
        # Log to wandb
        wandb.log({"Epoch": epoch + 1, "Train Loss": train_loss, "Train sMAPE": train_sMAPE, "Train MASE": train_MASE, 
                   "Validation Loss": val_loss, "Validation sMAPE": val_sMAPE, "Validation MASE": val_MASE,
                   #"Learning Rate": scheduler.get_last_lr()[0]
                   #"Loss alpha": criterion.logits[0],"Loss beta": criterion.logits[1],"Loss gamma": criterion.logits[2]
                   #,"Loss dealta": criterion.logits[3],"Loss lambda": criterion.logits[4]
                  })
        # Update progress bar
        pbar.set_description(f"Epoch {epoch + 1}/{num_epochs}" 
        +f"- Average Loss: {val_loss:.4f}" 
        #+f"- LR: {scheduler.get_last_lr()[0]:.6f}"
        )
   
    # Finalize wandb logging
    #wandb.finish()
    return model

def validate_model(model, val_loader, criterion, best_val_loss, processor_type, patience_counter, patience):
    model.eval()
    val_loss = 0.0
    val_sMAPE = 0.0
    val_MASE = 0.0
    with torch.no_grad():
        for batch_x, batch_y in val_loader:
            if batch_y.dim() == 1:
                batch_x = batch_x.to(device).unsqueeze(-1)
                batch_y = batch_y.to(device).unsqueeze(-1)
            elif batch_y.dim() > 1:
                batch_x = batch_x.to(device)
                batch_y = batch_y.to(device)
            else:
                print("The input doesn't fit the expected shape of a uni or multi variate Timeseries")
            decoder_input = torch.zeros_like(batch_y)
            decoder_input[:, 1:, :] = batch_y[:, :-1, :]
            decoder_input[:, 0, :] = batch_x[:, -1, :]
            with autocast(device_type= processor_type):
                output = model(batch_x, decoder_input)
                loss = criterion(output, batch_y)
                batch_sMAPE = smape(batch_y, output)
            val_loss += loss.item()
            val_sMAPE += batch_sMAPE
            #val_MASE += batch_MASE
                
        val_loss /= len(val_loader)
        val_sMAPE /= len(val_loader)
        val_MASE /= len(val_loader)
            # Early stopping check
    if val_loss < best_val_loss:
        best_val_loss = val_loss
        patience_counter = 0
    elif val_loss > (best_val_loss*1.025):
        patience_counter += 1
        if patience_counter >= patience:
            print(f"\nEarly stopping triggered after {epoch + 1} epochs")
            return False
    return True, best_val_loss, (val_loss, val_sMAPE, val_MASE)

# Positional Encoding class (unchanged)
class PositionalEncoding(nn.Module):
    def __init__(self, d_model, dropout=0.1, max_len=5000):
        super(PositionalEncoding, self).__init__()
        self.dropout = nn.Dropout(p=dropout)
        
        pe = torch.zeros(max_len, d_model)  # Create positional encoding matrix
        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-np.log(10000.0) / d_model))
        pe[:, 0::2] = torch.sin(position * div_term)  # Apply sine to even indices
        pe[:, 1::2] = torch.cos(position * div_term)  # Apply cosine to odd indices
        pe = pe.unsqueeze(0)
        self.register_buffer('pe', pe)  # Save positional encoding matrix

    def forward(self, x):
        x = x + self.pe[:, :x.size(1), :].to(x.device)  # Add positional encoding
        return self.dropout(x)