code: def PretrainedEmbeddingsField(
      default: Optional[str] = None,
      description: str = None,
      parameter_metadata: ParameterMetadata = None,
  ) -> Field:
      description = description or (
          "Path to a file containing pretrained embeddings. By default `dense` embeddings are initialized "
          "randomly, but this parameter allows to specify a path to a file containing embeddings in the "
          "[GloVe format](https://nlp.stanford.edu/projects/glove/). When the file containing the embeddings is "
          "loaded, only the embeddings with labels present in the vocabulary are kept, the others are discarded. "
          "If the vocabulary contains strings that have no match in the embeddings file, their embeddings are "
          "initialized with the average of all other embedding plus some random noise to make them different "
          "from each other. This parameter has effect only if `representation` is `dense`."
      )
      parameter_metadata = (
          parameter_metadata or COMMON_METADATA["pretrained_embeddings"]
      )
      return schema_utils.String(
          default=default,
          allow_none=True,
          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 13 for calling a global function with example usage: PretrainedEmbeddingsField(default, description, parameter_metadata) and returns: schema_utils