code: def autoencoder_loss(
      reconstruction: torch.Tensor,
      original_input: torch.Tensor,
      latent_activations: torch.Tensor,
      l1_weight: float,
  ) -> torch.Tensor:
      """
      :param reconstruction: output of Autoencoder.decode (shape: [batch, n_inputs])
      :param original_input: input of Autoencoder.encode (shape: [batch, n_inputs])
      :param latent_activations: output of Autoencoder.encode (shape: [batch, n_latents])
      :param l1_weight: weight of L1 loss
      :return: loss (shape: [1])
      """
      return (
          normalized_mean_squared_error(reconstruction, original_input)
          + normalized_L1_loss(latent_activations, original_input)
          * l1_weight
      )
  
imports: import torch
label_desc: Use image node_id 1 for calling a global function with example usage: autoencoder_loss(reconstruction, original_input, latent_activations, l1_weight) and returns: unknown