code: def _rotary_value_pe_mha(c: TransformerConfigs):
      from labml_nn.transformers.rope.value_pe import (
          RotaryValuePEMultiHeadAttention,
      )
  
      return RotaryValuePEMultiHeadAttention(
          c.n_heads, c.d_model, 1.0, 1.0
      )
  
file_path: annotated_deep_learning_paper_implementations/labml_nn/transformers/rope/value_pe/experiment.py
imports: from labml import experiment
  from labml.configs import calculate
  from labml_nn.transformers import TransformerConfigs
  from labml_nn.transformers.rope.experiment import Configs
label_desc: Use image node_id 1 for calling a global function with example usage: _rotary_value_pe_mha(c) and returns: RotaryValuePEMultiHeadAttention