code: def _symbolic_build(self, data_batch):
      model_unbuilt = not all(
          layer.built for layer in self._flatten_layers()
      )
      compile_metrics_unbuilt = (
          self._compile_metrics is not None
          and not self._compile_metrics.built
      )
      if model_unbuilt or compile_metrics_unbuilt:
          # Create symbolic tensors matching an input batch.
  
          def to_symbolic_input(v):
              if is_tensor(v):
                  return KerasTensor(
                      v.shape, standardize_dtype(v.dtype)
                  )
              return v
  
          data_batch = tree.map_structure(to_symbolic_input, data_batch)
          (
              x,
              y,
              sample_weight,
          ) = data_adapter_utils.unpack_x_y_sample_weight(data_batch)
          # Build all model state with `backend.compute_output_spec`.
          try:
              y_pred = backend.compute_output_spec(self, x)
          except:
              raise RuntimeError(
                  "Unable to automatically build the model. "
                  "Please build it yourself before calling "
                  "fit/evaluate/predict. "
                  "A model is 'built' when its variables have "
                  "been created and its `self.built` attribute "
                  "is True. Usually, calling the model on a batch "
                  "of data is the right way to build it."
              )
          if compile_metrics_unbuilt:
              # Build all metric state with `backend.compute_output_spec`.
              backend.compute_output_spec(
                  self.compute_metrics,
                  x,
                  y,
                  y_pred,
                  sample_weight=sample_weight,
              )
      self._post_build()
  
imports: import numpy
  import tree
  from keras import backend
  from keras import callbacks
  from keras.backend.common import standardize_dtype
  from keras.backend.common.keras_tensor import KerasTensor
  from keras.backend.numpy.core import is_tensor
  from keras.trainers import trainer
  from keras.trainers.data_adapters import data_adapter_utils
  from keras.trainers.epoch_iterator import EpochIterator
  from keras.utils import traceback_utils
label_desc: Use image node_id 6 for calling the NumpyTrainer obj's underlying member method code with example usage: obj._symbolic_build(data_batch) and returns: v, KerasTensor