code: def EmbeddingSizeField(
      default: int = 256,
      description: str = None,
      parameter_metadata: ParameterMetadata = None,
  ) -> Field:
      description = description or (
          "The maximum embedding size. The actual size will be `min(vocabulary_size, embedding_size)` for "
          "`dense` representations and exactly `vocabulary_size` for the `sparse` encoding, where `vocabulary_size` "
          "is the number of unique strings appearing in the training set input column plus the number of "
          "special tokens (`<UNK>`, `<PAD>`, `<SOS>`, `<EOS>`)."
      )
      parameter_metadata = (
          parameter_metadata or COMMON_METADATA["embedding_size"]
      )
      return schema_utils.PositiveInteger(
          default=default,
          description=description,
          parameter_metadata=parameter_metadata,
      )
  
imports: from dataclasses import Field
  from typing import Optional
  from ludwig.schema import utils
  from ludwig.schema.metadata import COMMON_METADATA
  from ludwig.schema.metadata.parameter_metadata import ParameterMetadata
  from ludwig.utils.torch_utils import initializer_registry
label_desc: Use image node_id 10 for calling a global function with example usage: EmbeddingSizeField(default, description, parameter_metadata) and returns: schema_utils