parameters: (weights, grads, mean, var, weights32, step_count,
                           lrs, wds, out=None, num_tensors=0, **kwargs)
return_statement: return ndarray._internal._multi_mp_lans_update(*temp_list,
                                                     out=out,
                                                     num_tensors=num_tensors,
                                                     step_count=step_count,
                                                     learning_rates=lrs,
                                                     wds=wds,
                                                     **kwargs)
docstring: Given a list of gradients, update weights, mean and variance of multiple tensors
      following LANS Optimizer implementation, and using Mixed-Precision.
  
      Parameters
      ----------
      weights : List of NDArrays containing the input weights of multiple tensors
  
      grads : List of NDArrays containing input gradients
  
      mean : List of NDArrays containing mean of multiple tensors to be updated
  
      var : List of NDArrays containing variance of multiple tensors to be updated
  
      weights32 : Master copy of weights in FP32
  
      step_count : List of scalars with the number of update step for each tensor
  
      lrs : List of learning rates (one for each tensor)
  
      wds : List of weight decays (one for each tensor)
  
      out: List of NDArrays where the updated weights will be stored
  
      num_tensors : Number of NDArrays/tensors in the list
docstring_summary: Given a list of gradients, update weights, mean and variance of multiple tensors
      following LANS Optimizer implementation, and using Mixed-Precision.
docstring_tokens: Given a list of gradients update weights mean and variance of multiple tensors following LANS Optimizer implementation and using Mixed - Precision .
function: def multi_mp_lans_update(weights, grads, mean, var, weights32, step_count,
                           lrs, wds, out=None, num_tensors=0, **kwargs):
      """Given a list of gradients, update weights, mean and variance of multiple tensors
      following LANS Optimizer implementation, and using Mixed-Precision.
  
      Parameters
      ----------
      weights : List of NDArrays containing the input weights of multiple tensors
  
      grads : List of NDArrays containing input gradients
  
      mean : List of NDArrays containing mean of multiple tensors to be updated
  
      var : List of NDArrays containing variance of multiple tensors to be updated
  
      weights32 : Master copy of weights in FP32
  
      step_count : List of scalars with the number of update step for each tensor
  
      lrs : List of learning rates (one for each tensor)
  
      wds : List of weight decays (one for each tensor)
  
      out: List of NDArrays where the updated weights will be stored
  
      num_tensors : Number of NDArrays/tensors in the list
      """
  
      if not num_tensors:
          num_tensors = len(weights)
      temp_list = _flatten_list(zip(weights, grads, mean, var, weights32))
      return ndarray._internal._multi_mp_lans_update(*temp_list,
                                                     out=out,
                                                     num_tensors=num_tensors,
                                                     step_count=step_count,
                                                     learning_rates=lrs,
                                                     wds=wds,
                                                     **kwargs)
function_tokens: def multi_mp_lans_update ( weights , grads , mean , var , weights32 , step_count , lrs , wds , out = None , num_tensors = 0 , * * kwargs ) : if not num_tensors : num_tensors = len ( weights ) temp_list = _flatten_list ( zip ( weights , grads , mean , var , weights32 ) ) return ndarray . _internal . _multi_mp_lans_update ( * temp_list , out = out , num_tensors = num_tensors , step_count = step_count , learning_rates = lrs , wds = wds , * * kwargs )