class_docstr: One MLP layer. It orders the operations as: norm -> fc -> act_fn -> dropout
docstr: Parameters
  ----------
  normalization
      Name of activation function.
  in_features
      Dimension of input features.
  out_features
      Dimension of output features.
  activation
      Name of activation function.
  dropout_prob
      Dropout probability.
docstr_tok: ["Parameters","--","--","--","--","--","normalization","Name","of","activation","function",".","in_features","Dimension","of","input","features",".","out_features","Dimension","of","output","features",".","activation","Name","of","activation","function",".","dropout_prob","Dropout","probability","."]
code: def __init__(
      self,
      normalization: str,
      in_features: int,
      out_features: int,
      activation: str,
      dropout_prob: float,
  ):
      """
      Parameters
      ----------
      normalization
          Name of activation function.
      in_features
          Dimension of input features.
      out_features
          Dimension of output features.
      activation
          Name of activation function.
      dropout_prob
          Dropout probability.
      """
      super().__init__()
      if normalization == "layer_norm":
          self.norm = nn.LayerNorm(in_features)
      elif normalization == "batch_norm":
          self.norm = nn.BatchNorm1d(in_features)
      elif normalization == "ghost_batch_norm":
          self.norm = GhostBatchNorm(in_features)
      else:
          raise ValueError(f"unknown normalization: {normalization}")
      self.fc = nn.Linear(in_features, out_features)
      self.act_fn = ALL_ACT_LAYERS[activation]()
      self.dropout = nn.Dropout(dropout_prob)
  
file_path: autogluon/multimodal/src/autogluon/multimodal/models/mlp.py
imports: from typing import Optional
  import numpy
  import torch
  from torch import nn
label_desc: Use image node_id 1 to create a new Unit object from inherited base classes: nn with example: obj = Unit(normalization, in_features, out_features, activation, dropout_prob)