code: def __init__(self, input_size: int, hidden_size: int):
      super().__init__()
      self.quantile_layer = nn.Sequential(
          nn.Linear(hidden_size, hidden_size),
          nn.PReLU(),
          nn.Linear(hidden_size, input_size),
      )
      self.output_layer = nn.Sequential(
          nn.Linear(input_size, input_size),
          nn.PReLU(),
          nn.Linear(input_size, 1),
      )
      self.register_buffer(
          "cos_multipliers", torch.arange(0, hidden_size) * torch.pi
      )
  
imports: from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import numpy
  from sklearn.base import BaseEstimator
  import torch
  from torch import distributions, nn
  import torch.nn.functional
  from pytorch_forecasting.data.encoders import TorchNormalizer, softplus_inv
  from pytorch_forecasting.metrics.base_metrics import DistributionLoss, MultivariateDistributionLoss
label_desc: Use image node_id 1 to create a new ImplicitQuantileNetwork object from inherited base classes: nn with example: obj = ImplicitQuantileNetwork(input_size, hidden_size)