docstr: Returns the augmentation pipeline for this model.
docstr_tok: ["Returns","the","augmentation","pipeline","for","this","model","."]
code: def get_augmentation_pipelines(self) -> AugmentationPipelines:
      """Returns the augmentation pipeline for this model."""
      # dictionary to hold any augmentation pipeline
      augmentation_pipelines = {}
  
      # loop through all input features and add their augmentation pipeline to the dictionary
      for input_feature in self.config_obj.input_features:
          # if augmentation was specified for this input feature, add AugmentationPipeline to dictionary
          if input_feature.has_augmentation():
              # use input feature proc_column as key because that is what is used in the Batcher
              augmentation_pipelines[
                  input_feature.proc_column
              ] = self.input_features.get(
                  input_feature.name
              ).get_augmentation_pipeline()
  
      return AugmentationPipelines(augmentation_pipelines)
  
imports: import logging
  import os
  from typing import Dict, Tuple, Union
  import numpy
  import torch
  from ludwig.accounting.used_tokens import get_used_tokens_for_ecd
  from ludwig.combiners.combiners import create_combiner
  from ludwig.constants import MODEL_ECD, MODEL_LLM, USED_TOKENS
  from ludwig.globals import MODEL_WEIGHTS_FILE_NAME
  from ludwig.models.base import BaseModel
  from ludwig.schema.model_types.ecd import ECDModelConfig
  from ludwig.utils import output_feature_utils
  from ludwig.utils.augmentation_utils import AugmentationPipelines
  from ludwig.utils.data_utils import clear_data_cache
  from ludwig.utils.fs_utils import open_file
  from ludwig.utils.state_dict_backward_compatibility import update_state_dict
  from ludwig.utils.torch_utils import get_torch_device
label_desc: Use image node_id 12 for calling the ECD obj's underlying member method code with example usage: obj.get_augmentation_pipelines() and returns: AugmentationPipelines