class_docstr: A simple Multilayer perceptron with 1 hidden layer.
  
  Attributes:
    hidden_size: The size of the hidden layer.
    output_size: The size of the output.
    activation: The activation function to apply to the hidden layer.
    dropout_rate: The dropout rate applied to the hidden layer.
    output_bias: If False, do not use a bias term in the last layer.
    deterministic: Disables dropout if set to True.
docstr: Applies the MLP to the last dimension of the inputs.
  
  Args:
    inputs: <float32>[batch_size, ..., input_features].
    deterministic: Disables dropout when set to True.
  
  Returns:
    The MLP output <float32>[batch_size, ..., output_size]
docstr_tok: ["Applies","the","MLP","to","the","last","dimension","of","the","inputs",".","Args",":","inputs",":","<","float32",">","[","batch_size",",","...",",","input_features","]",".","deterministic",":","Disables","dropout","when","set","to","True",".","Returns",":","The","MLP","output","<","float32",">","[","batch_size",",","...",",","output_size","]"]
code: def __call__(self, inputs: Array, deterministic: bool | None = None):
      """Applies the MLP to the last dimension of the inputs.
  
      Args:
        inputs: <float32>[batch_size, ..., input_features].
        deterministic: Disables dropout when set to True.
  
      Returns:
        The MLP output <float32>[batch_size, ..., output_size]
      """
      deterministic = nn.module.merge_param(
          "deterministic", self.deterministic, deterministic
      )
      hidden = self.intermediate_layer(inputs)
      hidden = self.activation(hidden)
      hidden = self.dropout_layer(hidden, deterministic=deterministic)
      output = self.output_layer(hidden)
      return output
  
imports: from __future__ import annotations
  import functools
  from typing import Any, Callable, Optional
  from flax import linen
  import jax
  from jax import numpy
  from jax._src.typing import Array
label_desc: Use image node_id 2 for calling the MLP obj's underlying member method code with example usage: obj.__call__(inputs, deterministic) and returns: output