code: def __init__(
      self,
      input_dim: int,
      output_dim: int,
      num_history: int = 1,
      *,
      latent_dim: int = 256,
      num_layers: int = 4,
      dropout: float = 0.0,
      norm_type: Optional[str] = "batch_norm",
      encoder_settings: Optional[Dict[str, MLEncoderSettings]] = None,
      global_encoder_settings: Optional[MLGlobalEncoderSettings] = None,
  ):
      super().__init__(
          input_dim,
          output_dim,
          num_history,
          latent_dim=latent_dim,
          token_mixing_type="mlp",
          num_layers=num_layers,
          dropout=dropout,
          norm_type=norm_type,
          feedforward_dim_ratio=1.0,
          use_head_token=False,
          use_positional_encoding=False,
          encoder_settings=encoder_settings,
          global_encoder_settings=global_encoder_settings,
      )
  
imports: import torch.nn
  from torch import Tensor
  from typing import Any
  from typing import Dict
  from typing import Optional
  from .base import MLModel
  from ...schema import MLEncoderSettings
  from ...schema import MLGlobalEncoderSettings
  from ...modules.blocks import Linear
  from ...modules.blocks import MixedStackedEncoder
label_desc: Use image node_id 1 to create a new Mixer object from inherited base classes: MixedStackedModel with example: obj = Mixer(input_dim, output_dim, num_history)