def forward(self, x):
    # Initialize hidden and cell states (if LSTM)
    h0 = torch.zeros(1, x.size(0), self.hidden_size).to(x.device)
    c0 = torch.zeros(1, x.size(0), self.hidden_size).to(x.device)

    # Forward propagate
    if isinstance(self.rnn, nn.LSTM):
        out, _ = self.rnn(x, (h0, c0))
    else:
        out, _ = self.rnn(x, h0)
    return out

def forward(self, src):
    # Assuming src is (batch_size, seq_len, input_size)
    src = src.permute(1, 0, 2)  # (seq_len, batch_size, input_size) for transformer input
    transformer_out = self.transformer(src)
    return transformer_out.permute(1, 0, 2)  # return to (batch_size, seq_len, input_size)

def forward(self, x):
    return F.relu(self.ss_layer(x))

    # Define three different architectures
    self.minimal_rnn = MinimalRNN(input_size, hidden_size, rnn_type='LSTM')
    self.transformer = TransformerModel(input_size)
    self.ssm = StateSpaceModel(input_size, hidden_size)

    # A linear layer for final output processing
    self.output_layer = nn.Linear(hidden_size, input_size)

def forward(self, x, task_type='simple'):
    # Dynamically choose architecture based on task type or sequence length
    if task_type == 'simple' or x.size(1) < self.task_threshold:  # Short sequence or simple task
        out = self.minimal_rnn(x)
    elif task_type == 'complex' or x.size(1) >= self.task_threshold:  # Long sequence or complex task
        out = self.transformer(x)
    else:  # Fall back to SSM for efficiency
        out = self.ssm(x)

    # Process the output through a final layer (common)
    out = self.output_layer(out)
    return out

# Create an instance of the Neural MorphGear
morph_gear = NeuralMorphGear(input_size=input_size, hidden_size=hidden_size, task_threshold=100)

# Input tensor (batch_size, seq_len, input_size)
x = torch.randn(batch_size, seq_len, input_size)

# Forward pass through the MorphGear
output = morph_gear(x, task_type='complex')

print("Output shape:", output.shape)

1.  Minimal RNN: This can either be an LSTM or a GRU. It handles shorter sequences or tasks where recurrence is efficient and sufficient.
2.  Transformer: This block is used for tasks that require handling long sequences or complex dependencies across the input. The transformer is configured with attention heads and encoder layers, making it ideal for capturing long-range dependencies.
3.  State-Space Model (SSM): A simplified state-space model for tasks where capturing patterns over long sequences efficiently is needed. It avoids the quadratic complexity of the transformer but offers efficiency over long sequences.
4.  Control Module: This module dynamically selects which architecture to use based on the input sequence length or a task-specific parameter (task_type). It acts like the gear mechanism of the MorphGear, shifting between RNN, Transformer, and SSM configurations.
5.  Task Threshold: A configurable threshold (e.g., 100 sequence length) determines when to switch between the minimal RNN and Transformer, simulating the “gearing” mechanism where the model adapts based on input complexity.

• For shorter sequences (seq_len < task_threshold) or simple tasks, the Minimal RNN is chosen for efficiency.
• For longer sequences or more complex tasks, the Transformer is chosen for handling long-range dependencies.
• If energy efficiency is prioritized (e.g., if the task is time-series or less complex), the State-Space Model (SSM) is activated.

def forward(self, x):
    # Initialize hidden and cell states (if LSTM)
    h0 = torch.zeros(1, x.size(0), self.hidden_size).to(x.device)
    c0 = torch.zeros(1, x.size(0), self.hidden_size).to(x.device)

    # Forward propagate
    if isinstance(self.rnn, nn.LSTM):
        out, _ = self.rnn(x, (h0, c0))
    else:
        out, _ = self.rnn(x, h0)
    return out

def forward(self, src):
    # Assuming src is (batch_size, seq_len, input_size)
    src = src.permute(1, 0, 2)  # (seq_len, batch_size, input_size) for transformer input
    transformer_out = self.transformer(src)
    return transformer_out.permute(1, 0, 2)  # return to (batch_size, seq_len, input_size)

def forward(self, x):
    return F.relu(self.ss_layer(x))

    # Define three different architectures
    self.minimal_rnn = MinimalRNN(input_size, hidden_size, rnn_type='LSTM')
    self.transformer = TransformerModel(input_size)
    self.ssm = StateSpaceModel(input_size, hidden_size)

    # A linear layer for final output processing
    self.output_layer = nn.Linear(hidden_size, input_size)

def forward(self, x, task_type='simple'):
    # Dynamically choose architecture based on task type or sequence length
    if task_type == 'simple' or x.size(1) < self.task_threshold:  # Short sequence or simple task
        out = self.minimal_rnn(x)
    elif task_type == 'complex' or x.size(1) >= self.task_threshold:  # Long sequence or complex task
        out = self.transformer(x)
    else:  # Fall back to SSM for efficiency
        out = self.ssm(x)

    # Process the output through a final layer (common)
    out = self.output_layer(out)
    return out

# Create an instance of the Neural MorphGear
morph_gear = NeuralMorphGear(input_size=input_size, hidden_size=hidden_size, task_threshold=100)

# Input tensor (batch_size, seq_len, input_size)
x = torch.randn(batch_size, seq_len, input_size)

# Forward pass through the MorphGear
output = morph_gear(x, task_type='complex')

print("Output shape:", output.shape)