code: def __call__(self, X, past=None):
      results = {}
      batch, sequence = X.shape
  
      past_length = 0 if past is None else past.shape[-2]
      h = np.take(self._wte.value, X, axis=0, mode="wrap") + np.take(
          self._wpe.value,
          positions_for(X, past_length),
          axis=0,
          mode="wrap",
      )
  
      # Transformer
      presents = []
      if past is not None:
          print(past.shape)
      pasts = (
          np.transpose(past, [1, 0, 2, 3, 4, 5])
          if past is not None
          else [None] * self.hparams.n_layer
      )
      assert len(pasts) == hparams.n_layer
      for block, past in zip(self.blocks, pasts):
          h, present = block(h, past=past)
          presents.append(present)
      results["present"] = np.stack(presents, axis=1)
      h = self.norm(h)
  
      # Language model loss.  Do tokens <n predict token n?
      h_flat = np.reshape(h, [batch * sequence, hparams.n_embd])
      logits = np.matmul(h_flat, np.transpose(self._wte.value))
      logits = np.reshape(logits, [batch, sequence, hparams.n_vocab])
      results["logits"] = logits
      return results
  
imports: import json
  import os
  import sys
  import jax.numpy
  import numpy
  import objax
  from objax.variable import TrainVar
label_desc: Use image node_id 2 for calling the Model obj's underlying member method code with example usage: obj.__call__(X, past) and returns: results