class_docstr: Single process, multiple threads distributed training.
  
  :param dnn: The dnn or the archive with the dnn to learn distributed.
  :type dnn: neoml.Dnn.Dnn or str
  :param type: Learn on cpu or gpu.
  :type type: str, ("cpu", "gpu"), default="cpu"
  :param count: Count of models to use.
  :type count: int, default=0
  :param devs: Numbers of gpus to use
  :type devs: list, default=None
  :param initializer: The initializer that will fill initial weight values.
  :type path: str, ("xavier", "xavier_uniform", "uniform"), default="xavier"
  :param seed: Random seed number.
  :type seed: int, default=42
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docstr: Sets the optimizer for the layer's trainable parameters.
  
  :param path: The full path to the location where the solver should be stored.
  :type path: str
docstr_tok: ["Sets","the","optimizer","for","the","layer","'s","trainable","parameters",".",":","param","path",":","The","full","path","to","the","location","where","the","solver","should","be","stored",".",":","type","path",":","str"]
code: def set_solver(self, path):
      """Sets the optimizer for the layer's trainable parameters.
  
      :param path: The full path to the location where the solver should be stored.
      :type path: str
      """
      self._set_solver(str(path))
  
file_path: neoml/NeoML/Python/neoml/Dnn/DnnDistributed.py
imports: import uuid
  import os
  from neoml.MathEngine import MathEngine
  import neoml.PythonWrapper
  from neoml.Blob import Blob
  from neoml.Dnn import Dnn
label_desc: Use image node_id 9 for calling the DnnDistributed obj's underlying member method code with example usage: obj.set_solver(path) without return types