class_docstr: Creates a feature embedding for the static categorical features.
class_docstr_tok: ["Creates","a","feature","embedding","for","the","static","categorical","features","."]
docstr: Parameters
  ----------
  features
      Input features to the model, shape: (-1, num_features).
  
  Returns
  -------
  torch.Tensor
      Embedding, shape: (-1, sum(self.embedding_dimensions)).
docstr_tok: ["Parameters","--","--","--","--","--","features","Input","features","to","the","model",",","shape",":","(","-1",",","num_features",")",".","Returns","--","--","--","-","torch.Tensor","Embedding",",","shape",":","(","-1",",","sum","(","self.embedding_dimensions",")",")","."]
code: def forward(self, features: torch.Tensor):
      """
      Parameters
      ----------
      features
          Input features to the model, shape: (-1, num_features).
  
      Returns
      -------
      torch.Tensor
          Embedding, shape: (-1, sum(self.embedding_dimensions)).
      """
      embedded_features = torch.cat(
          [
              embedder(features[:, i].long())
              for i, embedder in enumerate(self.embedders)
          ],
          dim=-1,
      )
      return embedded_features
  
imports: from functools import partial
  from typing import Optional, Callable, List, Union
  import torch
  from torch import nn
  from torch.distributions import Categorical, MixtureSameFamily, Normal
  from gluonts.core.component import validated
  from gluonts.torch.distributions import DiscreteDistribution
  from .scaling import min_max_scaling, standard_normal_scaling
label_desc: Use image node_id 2 for calling the FeatureEmbedder obj's underlying member method code with example usage: obj.forward(features) and returns: embedded_features