class_docstr: A class to generate Multi-Layer Perceptron.
      
class_docstr_tok: ["A","class","to","generate","Multi-Layer","Perceptron","."]
docstr: Initialize the instance.
  Args:
      n_output_node: An integer. Number of output nodes in the network.
      input_shape: A tuple. Input shape of the network. If it is 1D, ensure the value is appended by a comma
          in the tuple.
docstr_tok: ["Initialize","the","instance",".","Args",":","n_output_node",":","An","integer",".","Number","of","output","nodes","in","the","network",".","input_shape",":","A","tuple",".","Input","shape","of","the","network",".","If","it","is","1D",",","ensure","the","value","is","appended","by","a","comma","in","the","tuple","."]
code: def __init__(self, n_output_node, input_shape):
      """Initialize the instance.
      Args:
          n_output_node: An integer. Number of output nodes in the network.
          input_shape: A tuple. Input shape of the network. If it is 1D, ensure the value is appended by a comma
              in the tuple.
      """
      super(MlpGenerator, self).__init__(n_output_node, input_shape)
      if len(self.input_shape) > 1:
          raise ValueError("The input dimension is too high.")
  
file_path: nni/nni/algorithms/hpo/networkmorphism_tuner/nn.py
imports: from abc import abstractmethod
  from .graph import Graph
  from .layers import StubDense, StubDropout1d, StubReLU, get_batch_norm_class, get_conv_class, get_dropout_class, get_global_avg_pooling_class, get_pooling_class
  from .utils import Constant
label_desc: Use image node_id 1 to create a new MlpGenerator object from inherited base classes: NetworkGenerator with example: obj = MlpGenerator(n_output_node, input_shape)