class_docstr: Organises data transformations into a pipeline.
  
  Example
  -------
  >>> pipeline = DataPipeline(
  ...     static_data_keys=["text"],
  ...     dynamic_items=[
  ...     {"func": lambda x: x.lower(), "takes": "text", "provides": "foo"},
  ...     {"func": lambda x: x[::-1], "takes": "foo", "provides": "bar"},
  ...     ],
  ...     output_keys=["bar"],
  ... )
  >>> pipeline({"text": "Test"})
  {'bar': 'tset'}
class_docstr_tok: ["Organises","data","transformations","into","a","pipeline",".","Example","--","--","--","-",">",">",">","pipeline","=","DataPipeline","(","...","static_data_keys=","[","``","text","''","]",",","...","dynamic_items=","[","...","{","``","func","''",":","lambda","x",":","x.lower","(",")",",","``","takes","''",":","``","text","''",",","``","provides","''",":","``","foo","''","}",",","...","{","``","func","''",":","lambda","x",":","x","[",":",":-1","]",",","``","takes","''",":","``","foo","''",",","``","provides","''",":","``","bar","''","}",",","...","]",",","...","output_keys=","[","``","bar","''","]",",","...",")",">",">",">","pipeline","(","{","``","text","''",":","``","Test","''","}",")","{","'bar","'",":","'tset","'","}"]
code: def __init__(
      self, static_data_keys, dynamic_items=[], output_keys=[]
  ):
      self.dg = DependencyGraph()
      self._exec_order = None
      self.key_to_node = {}
      self.unaccounted_keys = {}
      self.dynamic_items = []
      self.output_mapping = {}
      self.add_static_keys(static_data_keys)
      self.add_dynamic_items(dynamic_items)
      self.set_output_keys(output_keys)
  
imports: import inspect
  from dataclasses import dataclass
  from speechbrain.utils.depgraph import DependencyGraph
label_desc: Use image node_id 1 to create a new DataPipeline object  with example: obj = DataPipeline(static_data_keys, dynamic_items, output_keys)