class_docstr: This class represents a collection of optimizers. The flow can optionally depend on other flows.
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docstr: Creates a new flow.
  
  Args:
      name (str): Unique name of the flow.
      optimizers (list, optional): List of optimizers.
      requires (list, optional): List (str) of flows which have to be applied before this flow. Defaults to None.
docstr_tok: ["Creates","a","new","flow",".","Args",":","name","(","str",")",":","Unique","name","of","the","flow",".","optimizers","(","list",",","optional",")",":","List","of","optimizers",".","requires","(","list",",","optional",")",":","List","(","str",")","of","flows","which","have","to","be","applied","before","this","flow",".","Defaults","to","None","."]
code: def __init__(self, name, optimizers, requires=None):
      """Creates a new flow.
  
      Args:
          name (str): Unique name of the flow.
          optimizers (list, optional): List of optimizers.
          requires (list, optional): List (str) of flows which have to be applied before this flow. Defaults to None.
      """
      self.name = name
      if optimizers is None:
          self._optimizers = []
      else:
          self._optimizers = optimizers
      if requires is None:
          self.requires = []
      else:
          self.requires = requires
  
file_path: hls4ml/hls4ml/model/flow/flow.py
label_desc: Use image node_id 1 to create a new Flow object  with example: obj = Flow(name, optimizers, requires)