code: def __init__(self, config: MLPEncoderConfig) -> None:
      TfModel.__init__(self, config)
      Encoder.__init__(self, config)
  
      # Create the neural network.
      self.net = TfMLP(
          input_dim=config.input_dims[0],
          hidden_layer_dims=config.hidden_layer_dims,
          hidden_layer_activation=config.hidden_layer_activation,
          hidden_layer_use_layernorm=config.hidden_layer_use_layernorm,
          hidden_layer_use_bias=config.hidden_layer_use_bias,
          hidden_layer_weights_initializer=config.hidden_layer_weights_initializer,
          hidden_layer_weights_initializer_config=(
              config.hidden_layer_weights_initializer_config
          ),
          hidden_layer_bias_initializer=config.hidden_layer_bias_initializer,
          hidden_layer_bias_initializer_config=(
              config.hidden_layer_bias_initializer_config
          ),
          output_dim=config.output_layer_dim,
          output_activation=config.output_layer_activation,
          output_use_bias=config.output_layer_use_bias,
          output_weights_initializer=config.output_layer_weights_initializer,
          output_weights_initializer_config=(
              config.output_layer_weights_initializer_config
          ),
          output_bias_initializer=config.output_layer_bias_initializer,
          output_bias_initializer_config=config.output_layer_bias_initializer_config,
      )
  
imports: from typing import Optional
  import tree
  from ray.rllib.core.models.base import Encoder, ActorCriticEncoder, StatefulActorCriticEncoder, STATE_IN, STATE_OUT, ENCODER_OUT, tokenize
  from ray.rllib.core.models.base import Model
  from ray.rllib.core.models.configs import ActorCriticEncoderConfig, CNNEncoderConfig, MLPEncoderConfig, RecurrentEncoderConfig
  from ray.rllib.core.models.tf.base import TfModel
  from ray.rllib.core.models.tf.primitives import TfMLP, TfCNN
  from ray.rllib.core.models.specs.specs_base import Spec
  from ray.rllib.core.models.specs.specs_dict import SpecDict
  from ray.rllib.core.models.specs.specs_base import TensorSpec
  from ray.rllib.models.utils import get_initializer_fn
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.framework import try_import_tf
  from ray.rllib.utils.nested_dict import NestedDict
label_desc: Use image node_id 1 to create a new TfMLPEncoder object from inherited base classes: Encoder, TfModel with example: obj = TfMLPEncoder(config)