class_docstr: A builder class that allows to builds a multitrack unet model
  estimator. The built model estimator has a different behaviour when
  used in a train/eval mode and in predict mode.
  
  * In train/eval mode:
      It takes as input and outputs magnitude spectrogram
  * In predict mode:
      It takes as input and outputs waveform.
      The whole separation process is then done in this function
      for performance reason: it makes it possible to run the whole
      separation process (including STFT and inverse STFT) on GPU.
  
  Example:
  >>> from spleeter.model import EstimatorSpecBuilder
  >>> builder = EstimatorSpecBuilder()
  >>> builder.build_predict_model()
  >>> builder.build_evaluation_model()
  >>> builder.build_train_model()
  
  >>> from spleeter.model import model_fn
  >>> estimator = tf.estimator.Estimator(model_fn=model_fn, ...)
class_docstr_tok: ["A","builder","class","that","allows","to","builds","a","multitrack","unet","model","estimator",".","The","built","model","estimator","has","a","different","behaviour","when","used","in","a","train\/eval","mode","and","in","predict","mode",".","*","In","train\/eval","mode",":","It","takes","as","input","and","outputs","magnitude","spectrogram","*","In","predict","mode",":","It","takes","as","input","and","outputs","waveform",".","The","whole","separation","process","is","then","done","in","this","function","for","performance","reason",":","it","makes","it","possible","to","run","the","whole","separation","process","(","including","STFT","and","inverse","STFT",")","on","GPU",".","Example",":",">",">",">","from","spleeter.model","import","EstimatorSpecBuilder",">",">",">","builder","=","EstimatorSpecBuilder","(",")",">",">",">","builder.build_predict_model","(",")",">",">",">","builder.build_evaluation_model","(",")",">",">",">","builder.build_train_model","(",")",">",">",">","from","spleeter.model","import","model_fn",">",">",">","estimator","=","tf.estimator.Estimator","(","model_fn=model_fn",",","...",")"]
docstr: Created a batch_sizexTxFxn_channels input tensor containing
  mix magnitude spectrogram, then an output dict from it
  according to the selected model in internal parameters.
  
  Raises:
      ValueError:
          If required model_type is not supported.
docstr_tok: ["Created","a","batch_sizexTxFxn_channels","input","tensor","containing","mix","magnitude","spectrogram",",","then","an","output","dict","from","it","according","to","the","selected","model","in","internal","parameters",".","Raises",":","ValueError",":","If","required","model_type","is","not","supported","."]
code: def _build_model_outputs(self):
      """
      Created a batch_sizexTxFxn_channels input tensor containing
      mix magnitude spectrogram, then an output dict from it
      according to the selected model in internal parameters.
  
      Raises:
          ValueError:
              If required model_type is not supported.
      """
      input_tensor = self.spectrogram_feature
      model = self._params.get("model", None)
      if model is not None:
          model_type = model.get("type", self.DEFAULT_MODEL)
      else:
          model_type = self.DEFAULT_MODEL
      try:
          apply_model = get_model_function(model_type)
      except ModuleNotFoundError:
          raise ValueError(f"No model function {model_type} found")
      self._model_outputs = apply_model(
          input_tensor,
          self._instruments,
          self._params["model"]["params"],
      )
  
imports: import importlib
  from typing import Any, Dict, Optional, Tuple
  import tensorflow
  from tensorflow.signal import hann_window, inverse_stft, stft
  from ..utils.tensor import pad_and_partition, pad_and_reshape
label_desc: Use image node_id 2 for calling the EstimatorSpecBuilder obj's underlying member method code with example usage: obj._build_model_outputs() without return types