class_docstr: VST inference modules, for models trained with `models/vst/vst.gin`.
class_docstr_tok: ["VST","inference","modules",",","for","models","trained","with","`","models\/vst\/vst.gin","`","."]
docstr: Parse the model operative config with special streaming parameters.
code: def parse_gin(self, ckpt):
      """Parse the model operative config with special streaming parameters."""
      parse_operative_config(ckpt)
  
      # Get Frame Size / Hop Size.
      self.frame_size = gin.query_parameter("%frame_size")
      frame_rate = gin.query_parameter("%frame_rate")
      self.sample_rate = gin.query_parameter("%sample_rate")
      self.hop_size = self.sample_rate // frame_rate
  
      # Get number of outputs.
      output_splits = dict(
          gin.query_parameter("RnnFcDecoder.output_splits")
      )
      self.n_harmonics = output_splits["harmonic_distribution"]
      self.n_noise = output_splits["noise_magnitudes"]
  
      # Get RNN dimesnions.
      self.state_size = gin.query_parameter("RnnFcDecoder.rnn_channels")
  
      # Get interpolation method.
      self.resample_method = gin.query_parameter(
          "Harmonic.amp_resample_method"
      )
  
imports: import ddsp
  from ddsp.training import models
  from ddsp.training import train_util
  import gin
  import tensorflow
label_desc: Use image node_id 2 for calling the VSTBaseModule obj's underlying member method code with example usage: obj.parse_gin(ckpt) without return types