parameters: (
          self,
          rank,
          trainer_count,
          use_cuda_rpc
      )
docstring: r"""
          A parameter server that averages the gradients
          from trainers for each training iteration step.
          Gradients are stored and averaged when a gradient
          has been received from each trainer for a param
          location.
          Args:
              rank (int): worker rank
              trainer_count (int): count of trainers sending
                  gradients to the server
              use_cuda_rpc (bool): indicator for CUDA RPC
docstring_summary: r"""
          A parameter server that averages the gradients
          from trainers for each training iteration step.
          Gradients are stored and averaged when a gradient
          has been received from each trainer for a param
          location.
          Args:
              rank (int): worker rank
              trainer_count (int): count of trainers sending
                  gradients to the server
              use_cuda_rpc (bool): indicator for CUDA RPC
docstring_tokens: r A parameter server that averages the gradients from trainers for each training iteration step . Gradients are stored and averaged when a gradient has been received from each trainer for a param location . Args : rank ( int ) : worker rank trainer_count ( int ) : count of trainers sending gradients to the server use_cuda_rpc ( bool ) : indicator for CUDA RPC
function: def __init__(
          self,
          rank,
          trainer_count,
          use_cuda_rpc
      ):
          r"""
          A parameter server that averages the gradients
          from trainers for each training iteration step.
          Gradients are stored and averaged when a gradient
          has been received from each trainer for a param
          location.
          Args:
              rank (int): worker rank
              trainer_count (int): count of trainers sending
                  gradients to the server
              use_cuda_rpc (bool): indicator for CUDA RPC
          """
          super().__init__(rank, trainer_count, use_cuda_rpc)
function_tokens: def __init__ ( self , rank , trainer_count , use_cuda_rpc ) : super ( ) . __init__ ( rank , trainer_count , use_cuda_rpc )