class_docstr: DefaultNet variant that uses qiskit to add a final quantum layer
class_docstr_tok: ["DefaultNet","variant","that","uses","qiskit","to","add","a","final","quantum","layer"]
code: def __init__(
      self,
      input_size: int = None,
      output_size: int = None,
      shape: list = None,
      max_params: int = 3e7,
      num_hidden: int = 1,
      dropout: float = 0,
  ) -> None:
      hidden_size = max([input_size * 2, output_size * 2, 400])
      super().__init__(
          input_size=input_size,
          output_size=hidden_size,
          shape=shape,
          max_params=max_params,
          num_hidden=num_hidden,
          dropout=dropout,
      )
      self.fc = torch.nn.Linear(hidden_size, output_size)
      self.hybrid = HybridSingle(
          qiskit.Aer.get_backend("aer_simulator"), 100, np.pi / 2
      )
  
imports: import torch
  import qiskit
  import numpy
  from lightwood.mixer.helpers.default_net import DefaultNet
  from lightwood.helpers.torch import LightwoodAutocast
label_desc: Use image node_id 1 to create a new QClassicNet object from inherited base classes: DefaultNet with example: obj = QClassicNet(input_size, output_size, shape, max_params, num_hidden, dropout)