class_docstr: Multi-Layer Perceptron with custom parameters.
  It can be configured to have multiple layers and dropout.
  
  **Example**::
  
      >>> from avalanche.models import SimpleMLP
      >>> n_classes = 10 # e.g. MNIST
      >>> model = SimpleMLP(num_classes=n_classes)
      >>> print(model) # View model details
class_docstr_tok: ["Multi-Layer","Perceptron","with","custom","parameters",".","It","can","be","configured","to","have","multiple","layers","and","dropout",".","*","*","Example","*","*",":",":",">",">",">","from","avalanche.models","import","SimpleMLP",">",">",">","n_classes","=","10","#","e.g",".","MNIST",">",">",">","model","=","SimpleMLP","(","num_classes=n_classes",")",">",">",">","print","(","model",")","#","View","model","details"]
code: def get_features(self, x):
      x = x.contiguous()
      x = x.view(x.size(0), self._input_size)
      x = self.features(x)
      return x
  
file_path: avalanche/avalanche/models/simple_mlp.py
imports: import torch.nn
  from avalanche.models.dynamic_modules import MultiTaskModule, MultiHeadClassifier
  from avalanche.models.base_model import BaseModel
label_desc: Use image node_id 3 for calling the SimpleMLP obj's underlying member method code with example usage: obj.get_features(x) and returns: x