code: def test_ImplicitQuantileNetworkDistributionLoss():
      batch_size = 3
      n_timesteps = 2
      output_size = 5
  
      target = torch.rand((batch_size, n_timesteps))
  
      normalizer = TorchNormalizer(
          center=True, transformation="softplus"
      )
      normalizer.fit(target.reshape(-1))
  
      loss = ImplicitQuantileNetworkDistributionLoss(
          input_size=output_size
      )
      x = torch.rand((batch_size, n_timesteps, output_size))
      target_scale = torch.rand((batch_size, 2))
      pred = loss.rescale_parameters(
          x, target_scale=target_scale, encoder=normalizer
      )
      assert loss.loss(pred, target).shape == target.shape
      quantiles = loss.to_quantiles(pred)
      assert quantiles.size(-1) == len(loss.quantiles)
      assert quantiles.size(0) == batch_size
      assert quantiles.size(1) == n_timesteps
  
      point_prediction = loss.to_prediction(pred, n_samples=None)
      assert (
          point_prediction.ndim
          == loss.to_prediction(pred, n_samples=100).ndim
      )
  
file_path: pytorch-forecasting/tests/test_metrics.py
imports: import itertools
  import numpy
  import pytest
  import torch
  from torch.nn.utils import rnn
  from pytorch_forecasting.data.encoders import TorchNormalizer
  from pytorch_forecasting.metrics import MAE, SMAPE, BetaDistributionLoss, ImplicitQuantileNetworkDistributionLoss, LogNormalDistributionLoss, MultivariateNormalDistributionLoss, NegativeBinomialDistributionLoss, NormalDistributionLoss
  from pytorch_forecasting.metrics.base_metrics import AggregationMetric, CompositeMetric
label_desc: Use image node_id 9 for calling a global function with example usage: test_ImplicitQuantileNetworkDistributionLoss() without return types