code: def EmbeddingsTrainableField(
      default: bool = True,
      description: str = None,
      parameter_metadata: ParameterMetadata = None,
  ) -> Field:
      description = description or (
          "If `true` embeddings are trained during the training process, if `false` embeddings are fixed. "
          "It may be useful when loading pretrained embeddings for avoiding finetuning them. This parameter "
          "has effect only when `representation` is `dense`; `sparse` one-hot encodings are not trainable."
      )
      parameter_metadata = (
          parameter_metadata or COMMON_METADATA["embeddings_trainable"]
      )
      return schema_utils.Boolean(
          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 12 for calling a global function with example usage: EmbeddingsTrainableField(default, description, parameter_metadata) and returns: schema_utils