class_docstr: Beta distribution loss for unit interval data.
  
  Requirements for original target normalizer:
      * logit transformation
class_docstr_tok: ["Beta","distribution","loss","for","unit","interval","data",".","Requirements","for","original","target","normalizer",":","*","logit","transformation"]
code: def map_x_to_distribution(
      self, x: torch.Tensor
  ) -> distributions.Beta:
      mean = x[..., 0]
      shape = x[..., 1]
      return self.distribution_class(
          concentration0=(1 - mean) * shape, concentration1=mean * shape
      )
  
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 BetaDistributionLoss obj's underlying member method code with example usage: obj.map_x_to_distribution(x) and returns: self