code: def __call__(self, x, past):
      assert len(x.shape) == 3  # Should be [batch, sequence, features]
      assert self.n_state % hparams.n_head == 0
      if past is not None:
          assert (
              len(past.shape) == 5
          )  # Should be [batch, 2, heads, sequence, features], where 2 is [k, v]
  
      def split_heads(x):
          # From [batch, sequence, features] to [batch, heads, sequence, features]
          return np.transpose(
              split_states(x, hparams.n_head), [0, 2, 1, 3]
          )
  
      def merge_heads(x):
          # Reverse of split_heads
          return merge_states(np.transpose(x, [0, 2, 1, 3]))
  
      def mask_attn_weights(w):
          # w has shape [batch, heads, dst_sequence, src_sequence], where information flows from src to dst.
          _, _, nd, ns = w.shape
          b = attention_mask(nd, ns, dtype=w.dtype)
          b = np.reshape(b, [1, 1, nd, ns])
          w = w * b - np.array(1e10, w.dtype) * (1 - b)
          return w
  
      def multihead_attn(q, k, v):
          # q, k, v have shape [batch, heads, sequence, features]
          w = np.matmul(q, np.transpose(k, [0, 1, 3, 2]))
          w = w * objax.functional.rsqrt(np.array(v.shape[-1], w.dtype))
  
          w = mask_attn_weights(w)
          w = objax.functional.softmax(w)
          a = np.matmul(w, v)
          return a
  
      c = self.conv1(x)
      q, k, v = map(split_heads, np.split(c, 3, axis=2))
      present = np.stack([k, v], axis=1)
      if past is not None:
          pk, pv = past[:, 0], past[:, 1]
          k = np.concatenate([pk, k], axis=-2)
          v = np.concatenate([pv, v], axis=-2)
      a = multihead_attn(q, k, v)
      a = merge_heads(a)
      a = self.conv2(a)
      return a, present
  
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 Attn obj's underlying member method code with example usage: obj.__call__(x, past) and returns: a,  present, np, merge_states, w, a