class_docstr: Feature encoder for NetworkX representations of TypeDB data. Type data is assumed always present for each node
  and edge, consistent with the TypeDB knowledge model. Therefore, this encoder provides a de-factor method to
  embed that type information. Supply attribute-specific encoders to handle encoding values of attributes,
  since the meaning of these values is domain-dependent.
class_docstr_tok: ["Feature","encoder","for","NetworkX","representations","of","TypeDB","data",".","Type","data","is","assumed","always","present","for","each","node","and","edge",",","consistent","with","the","TypeDB","knowledge","model",".","Therefore",",","this","encoder","provides","a","de-factor","method","to","embed","that","type","information",".","Supply","attribute-specific","encoders","to","handle","encoding","values","of","attributes",",","since","the","meaning","of","these","values","is","domain-dependent","."]
code: def __init__(
      self,
      node_types,
      edge_types,
      type_encoding_size,
      attribute_encoders,
      attribute_encoding_size,
  ):
      self.node_types = node_types
      self.edge_types = edge_types
      self.attribute_encoders = attribute_encoders
      self.attribute_encoding_size = attribute_encoding_size
      self.node_type_embedding = Embedding(
          len(self.node_types), type_encoding_size
      )
      self.edge_type_embedding = Embedding(
          len(self.edge_types), type_encoding_size
      )
  
file_path: typedb-ml/typedb_ml/pytorch_geometric/transform/encode.py
imports: from typing import List
  import torch
  from torch.nn import Embedding
  from typedb_ml.networkx.iterate import multidigraph_node_data_iterator, multidigraph_edge_data_iterator
label_desc: Use image node_id 1 to create a new FeatureEncoder object  with example: obj = FeatureEncoder(node_types, edge_types, type_encoding_size, attribute_encoders, attribute_encoding_size)