class_docstr: Multi-Layer Perceptron Class
  
  **Parameters:**<br>
  `in_features`: int, dimension of input.<br>
  `out_features`: int, dimension of output.<br>
  `activation`: str, activation function to use.<br>
  `hidden_size`: int, dimension of hidden layers.<br>
  `num_layers`: int, number of hidden layers.<br>
  `dropout`: float, dropout rate.<br>
class_docstr_tok: ["Multi-Layer","Perceptron","Class","*","*","Parameters",":","*","*","<","br",">","`","in_features","`",":","int",",","dimension","of","input.","<","br",">","`","out_features","`",":","int",",","dimension","of","output.","<","br",">","`","activation","`",":","str",",","activation","function","to","use.","<","br",">","`","hidden_size","`",":","int",",","dimension","of","hidden","layers.","<","br",">","`","num_layers","`",":","int",",","number","of","hidden","layers.","<","br",">","`","dropout","`",":","float",",","dropout","rate.","<","br",">"]
code: def forward(self, x):
      return self.layers(x)
  
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the MLP obj's underlying member method code with example usage: obj.forward(x) and returns: self