code: def call(self, x, **kwargs):
      b, hh, ww, c, u, h = *x.get_shape().as_list(), self.u, self.heads
  
      q = self.to_q(x)
      k = self.to_k(x)
      v = self.to_v(x)
  
      q = self.norm_q(q)
      v = self.norm_v(v)
  
      q = Rearrange("b hh ww (h k) -> b h k (hh ww)", h=h)(q)
      k = Rearrange("b hh ww (u k) -> b u k (hh ww)", u=u)(k)
      v = Rearrange("b hh ww (u v) -> b u v (hh ww)", u=u)(v)
  
      k = nn.softmax(k)
  
      Lc = einsum("b u k m, b u v m -> b k v", k, v)
      Yc = einsum("b h k n, b k v -> b n h v", q, Lc)
  
      if self.local_contexts:
          v = Rearrange("b u v (hh ww) -> b v hh ww u", hh=hh, ww=ww)(v)
          Lp = self.pos_conv(v)
          Lp = Rearrange("b v h w k -> b v k (h w)")(Lp)
          Yp = einsum("b h k n, b v k n -> b n h v", q, Lp)
      else:
          rel_pos_emb = tf.gather_nd(self.rel_pos_emb, self.rel_pos)
          Lp = einsum("n m k u, b u v m -> b n k v", rel_pos_emb, v)
          Yp = einsum("b h k n, b n k v -> b n h v", q, Lp)
  
      Y = Yc + Yp
      out = Rearrange("b (hh ww) h v -> b hh ww (h v)", hh=hh, ww=ww)(Y)
      return out
  
file_path: lambda-networks/lambda_networks/tfkeras.py
imports: import tensorflow
  from einops.layers.tensorflow import Rearrange
  from tensorflow.keras.layers import Conv2D, BatchNormalization, Conv3D, ZeroPadding3D, Softmax, Lambda, Add, Layer
  from tensorflow.keras import initializers
  from tensorflow import einsum, nn, meshgrid
label_desc: Use image node_id 2 for calling the LambdaLayer obj's underlying member method code with example usage: obj.call(x) and returns: out