class_docstr: Log-normal loss.
  
  Requirements for original target normalizer:
      * normalized target in log space
class_docstr_tok: ["Log-normal","loss",".","Requirements","for","original","target","normalizer",":","*","normalized","target","in","log","space"]
code: def map_x_to_distribution(
      self, x: torch.Tensor
  ) -> distributions.LogNormal:
      return self.distribution_class(loc=x[..., 0], scale=x[..., 1])
  
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 for calling the LogNormalDistributionLoss obj's underlying member method code with example usage: obj.map_x_to_distribution(x) and returns: self