class_docstr: Fully-connected state-input Q-function with discrete actions.
  
  Args:
      n_dim_obs: number of dimensions of observation space
      n_actions (int): Number of actions in action space.
      n_hidden_channels: number of hidden channels
      n_hidden_layers: number of hidden layers
      nonlinearity (callable): Nonlinearity applied after each hidden layer.
      last_wscale (float): Weight scale of the last layer.
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code: def __init__(
      self,
      ndim_obs,
      n_actions,
      n_hidden_channels,
      n_hidden_layers,
      nonlinearity=F.relu,
      last_wscale=1.0,
  ):
      super().__init__(
          model=MLP(
              in_size=ndim_obs,
              out_size=n_actions,
              hidden_sizes=[n_hidden_channels] * n_hidden_layers,
              nonlinearity=nonlinearity,
              last_wscale=last_wscale,
          )
      )
  
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from pfrl.action_value import DiscreteActionValue, DistributionalDiscreteActionValue, QuadraticActionValue
  from pfrl.functions.lower_triangular_matrix import lower_triangular_matrix
  from pfrl.initializers import init_chainer_default
  from pfrl.nn import Lambda
  from pfrl.nn.mlp import MLP
  from pfrl.q_function import StateQFunction
label_desc: Use image node_id 1 to create a new FCStateQFunctionWithDiscreteAction object from inherited base classes: SingleModelStateQFunctionWithDiscreteAction with example: obj = FCStateQFunctionWithDiscreteAction(ndim_obs, n_actions, n_hidden_channels, n_hidden_layers, nonlinearity, last_wscale)