class_docstr: Embed Dict of feature_id -> feature value to list of tensors (1 tensor per feature ID) after looking up feature value embedding,
  then apply optional pooling and MLP to final tensor.
  Passed in feature dict keys need to be in fixed order in forward.
docstr: Factory method to construct an instance of DictEmbedding from
  the module's config object and the field's metadata object.
  
  Args:
      config (DictFeatConfig): Configuration object specifying all the
      parameters of DictEmbedding.
      metadata (FieldMeta): Object containing this field's metadata.
  
  Returns:
      type: An instance of DictEmbedding.
code: def from_config(cls, config: Config):
      """Factory method to construct an instance of DictEmbedding from
      the module's config object and the field's metadata object.
  
      Args:
          config (DictFeatConfig): Configuration object specifying all the
          parameters of DictEmbedding.
          metadata (FieldMeta): Object containing this field's metadata.
  
      Returns:
          type: An instance of DictEmbedding.
  
      """
      return cls(
          embedding_dim=config.embedding_dim,
          pooling_type=config.pooling_type,
          mlp_layer_dims=config.mlp_layer_dims,
          feature_buckets=config.feature_buckets,
      )
  
file_path: pytext/pytext/models/embeddings/int_single_category_embedding.py
imports: from typing import Dict, List
  import torch
  import torch.nn
  from pytext.config.module_config import ModuleConfig
  from pytext.utils.usage import log_class_usage
  from .embedding_base import EmbeddingBase
label_desc: Use image node_id 1 for calling the IntSingleCategoryEmbedding obj's underlying member method code with example usage: obj.from_config(cls, config) and returns: cls