code: def forward(self, x, context=None, mask=None):
      batch_size, sequence_length, dim = x.shape
  
      h = self.heads
  
      q = self.to_q(x)
      context = default(context, x)
      k = self.to_k(context)
      v = self.to_v(context)
  
      q = self.reshape_heads_to_batch_dim(q)
      k = self.reshape_heads_to_batch_dim(k)
      v = self.reshape_heads_to_batch_dim(v)
  
      sim = paddle.einsum("b i d, b j d -> b i j", q * self.scale, k)
  
      if exists(mask):
          mask = mask.reshape([batch_size, -1])
          max_neg_value = -paddle.finfo(sim.dtype).max
          mask = mask[:, None, :].repeat(h, 1, 1)
          sim.masked_fill_(~mask, max_neg_value)
  
      # attention, what we cannot get enough of
      attn = F.softmax(sim, axis=-1)
  
      out = paddle.einsum("b i j, b j d -> b i d", attn, v)
      out = self.reshape_batch_dim_to_heads(out)
      return self.to_out(out)
  
imports: import math
  from inspect import isfunction
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
label_desc: Use image node_id 4 for calling the CrossAttention obj's underlying member method code with example usage: obj.forward(x, context, mask) and returns: self