code: def model(hparams, X, past=None, scope="model", gpus=[], reuse=False):
      with tf.compat.v1.variable_scope(scope, reuse=reuse):
          results = {}
          batch, sequence = shape_list(X)
  
          wpe = tf.compat.v1.get_variable(
              "wpe",
              [hparams.n_ctx, hparams.n_embd],
              initializer=tf.compat.v1.random_normal_initializer(
                  stddev=0.01
              ),
          )
          wte = tf.compat.v1.get_variable(
              "wte",
              [hparams.n_vocab, hparams.n_embd],
              initializer=tf.compat.v1.random_normal_initializer(
                  stddev=0.02
              ),
          )
          past_length = 0 if past is None else tf.shape(input=past)[-2]
          h = tf.gather(wte, X) + tf.gather(
              wpe, positions_for(X, past_length)
          )
  
          # Transformer
          presents = []
          pasts = (
              tf.unstack(past, axis=1)
              if past is not None
              else [None] * hparams.n_layer
          )
          assert len(pasts) == hparams.n_layer
          gpu_stack = (
              np.floor(hparams.n_layer / len(gpus))
              if len(gpus) > 0
              else 0
          )
          d = 0
          for layer, past in enumerate(pasts):
              if gpu_stack < 1:
                  h, present = block(
                      h, "h%d" % layer, past=past, hparams=hparams
                  )
                  tf.compat.v1.add_to_collection("checkpoints", h)
                  presents.append(present)
              else:
                  if (
                      layer != 0
                      and layer % gpu_stack == 0
                      and d + 1 != len(gpus)
                  ):
                      d += 1
                  with tf.device(gpus[d]):
                      h, present = block(
                          h, "h%d" % layer, past=past, hparams=hparams
                      )
                      tf.compat.v1.add_to_collection("checkpoints", h)
                      presents.append(present)
          results["present"] = tf.stack(presents, axis=1)
          h = norm(h, "ln_f")
  
          # Language model loss.  Do tokens <n predict token n?
          h_flat = tf.reshape(h, [batch * sequence, hparams.n_embd])
          logits = tf.matmul(h_flat, wte, transpose_b=True)
          logits = tf.reshape(
              logits, [batch, sequence, hparams.n_vocab]
          )
          results["logits"] = logits
          return results
  
imports: import numpy
  import tensorflow
label_desc: Use image node_id 17 for calling a global function with example usage: model(hparams, X, past, scope, gpus, reuse) and returns: results