code: def _predict_embeddings(self, data, params):
      from mlflow.openai.api_request_parallel_processor import (
          process_api_requests,
      )
  
      _validate_model_params(self.task, self.model, params)
      batch_size = params.pop("batch_size", self.api_config.batch_size)
      _logger.debug(
          f"Requests are being batched by {batch_size} samples."
      )
  
      first_string_column = _first_string_column(data)
      texts = data[first_string_column].tolist()
      requests = [
          {
              **self.model,
              **params,
              "input": texts[i : i + batch_size],
          }
          for i in range(0, len(texts), batch_size)
      ]
      request_url = self._construct_request_url(
          "embeddings", REQUEST_URL_EMBEDDINGS
      )
  
      results = process_api_requests(
          requests,
          request_url,
          api_token=self.api_token,
          max_requests_per_minute=self.api_config.max_requests_per_minute,
          max_tokens_per_minute=self.api_config.max_tokens_per_minute,
      )
      return [
          row["embedding"] for batch in results for row in batch["data"]
      ]
  
imports: import itertools
  import logging
  import os
  import warnings
  from string import Formatter
  from typing import Any, Dict, Optional, Set
  import yaml
  import mlflow
  from mlflow import pyfunc
  from mlflow.environment_variables import _MLFLOW_TESTING, MLFLOW_OPENAI_SECRET_SCOPE
  from mlflow.exceptions import MlflowException
  from mlflow.models import Model, ModelInputExample, ModelSignature
  from mlflow.models.model import MLMODEL_FILE_NAME
  from mlflow.models.utils import _save_example
  from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE
  from mlflow.tracking._model_registry import DEFAULT_AWAIT_MAX_SLEEP_SECONDS
  from mlflow.tracking.artifact_utils import _download_artifact_from_uri
  from mlflow.types import ColSpec, Schema, TensorSpec
  from mlflow.utils.annotations import experimental
  from mlflow.utils.databricks_utils import check_databricks_secret_scope_access, is_in_databricks_runtime
  from mlflow.utils.docstring_utils import LOG_MODEL_PARAM_DOCS, format_docstring
  from mlflow.utils.environment import _CONDA_ENV_FILE_NAME, _CONSTRAINTS_FILE_NAME, _PYTHON_ENV_FILE_NAME, _REQUIREMENTS_FILE_NAME, _mlflow_conda_env, _process_conda_env, _process_pip_requirements, _PythonEnv, _validate_env_arguments
  from mlflow.utils.file_utils import write_to
  from mlflow.utils.model_utils import _add_code_from_conf_to_system_path, _get_flavor_configuration, _validate_and_copy_code_paths, _validate_and_prepare_target_save_path
  from mlflow.utils.openai_utils import REQUEST_URL_CHAT, REQUEST_URL_COMPLETIONS, REQUEST_URL_EMBEDDINGS, _OAITokenHolder, _OpenAIApiConfig, _OpenAIEnvVar, _validate_model_params
  from mlflow.utils.requirements_utils import _get_pinned_requirement
label_desc: Use image node_id 8 for calling the _OpenAIWrapper obj's underlying member method code with example usage: obj._predict_embeddings(data, params) and returns: unknown