class_docstr: Embed Dict of feature_id -> (feature values, offsets, weights) to list of tensors (1 tensor per feature ID) with EmbeddingBag,
  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 IntWeightedMultiCategoryEmbedding
  from the module's config object and the field's metadata object.
  
  Args:
      config (Config): Configuration object specifying all the
      parameters of IntWeightedMultiCategoryEmbedding.
      num_intput_features: Number of input features in forward.
  
  Returns:
      type: An instance of IntWeightedMultiCategoryEmbedding.
code: def from_config(cls, config: Config):
      """Factory method to construct an instance of IntWeightedMultiCategoryEmbedding
      from the module's config object and the field's metadata object.
  
      Args:
          config (Config): Configuration object specifying all the
          parameters of IntWeightedMultiCategoryEmbedding.
          num_intput_features: Number of input features in forward.
  
      Returns:
          type: An instance of IntWeightedMultiCategoryEmbedding.
  
      """
      return cls(
          embedding_dim=config.embedding_dim,
          weight_scale=config.weight_scale,
          features_embedding_bag_mode=config.features_embedding_bag_mode,
          embedding_bag_mode=config.embedding_bag_mode,
          ignore_weight=config.ignore_weight,
          pooling_type=config.pooling_type,
          mlp_layer_dims=config.mlp_layer_dims,
          feature_buckets=config.feature_buckets,
      )
  
file_path: pytext/pytext/models/embeddings/int_weighted_multi_category_embedding.py
imports: from typing import Dict, List, Optional, Tuple
  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 IntWeightedMultiCategoryEmbedding obj's underlying member method code with example usage: obj.from_config(cls, config) and returns: cls