code: def block(
      x,
      n_head,
      act_fn,
      resid_pdrop,
      attn_pdrop,
      scope,
      train=False,
      scale=False,
      explain=False,
  ):
      with tf.compat.v1.variable_scope(scope):
          nx = shape_list(x)[-1]
          a = attn(
              x,
              "attn",
              nx,
              n_head,
              resid_pdrop,
              attn_pdrop,
              train=train,
              scale=scale,
              explain=explain,
          )
          n = norm(x + a, "ln_1")
          m = mlp(n, "mlp", nx * 4, act_fn, resid_pdrop, train=train)
          h = norm(n + m, "ln_2")
          return h
  
imports: import functools
  import numpy
  import tensorflow
  from finetune.optimizers.recompute_grads import recompute_grad
  from finetune.util.shapes import shape_list, lengths_from_eos_idx
  from finetune.nn.activations import act_fns
  from finetune.nn.nn_utils import dropout, norm
label_desc: Use image node_id 14 for calling a global function with example usage: block(x, n_head, act_fn, resid_pdrop, attn_pdrop, scope, train, scale, explain) and returns: h