code: def get_data_provider(dataset_path, model_path):
      """Get the data provider for dataset for statistics.
  
      Read TF examples from specified path if provided, else use the
      data provider specified in the gin config.
      Args:
        dataset_path: Path to an sstable of TF Examples.
        model_path: Path to the model checkpoint dir containing the gin config.
      Returns:
        Data provider to calculate statistics over.
      """
      # First, see if the dataset path is specified
      if dataset_path is not None:
          dataset_path = train_util.expand_path(dataset_path)
          return data.TFRecordProvider(dataset_path)
      else:
          inference.parse_operative_config(model_path)
          try:
              dp_binding = gin.query_parameter("train.data_provider")
              return dp_binding.scoped_configurable_fn()
  
          except ValueError as e:
              raise Exception(
                  "Failed to parse dataset from gin. Either --dataset_path "
                  "or train.data_provider gin param must be set."
              ) from e
  
imports: import datetime
  import json
  import os
  from absl import app
  from absl import flags
  import ddsp
  from ddsp.training import data
  from ddsp.training import inference
  from ddsp.training import postprocessing
  from ddsp.training import train_util
  import gin
  import librosa
  import note_seq
  import tensorflow
  from tensorflowjs.converters import converter
  from tflite_support import metadata
label_desc: Use image node_id 1 for calling a global function with example usage: get_data_provider(dataset_path, model_path) and returns: data, dp_binding