class_docstr: Transformer block for image-like data. First, project the input (aka embedding) and reshape to b, t, d. Then apply
  standard transformer action. Finally, reshape to image
class_docstr_tok: ["Transformer","block","for","image-like","data",".","First",",","project","the","input","(","aka","embedding",")","and","reshape","to","b",",","t",",","d.","Then","apply","standard","transformer","action",".","Finally",",","reshape","to","image"]
code: def forward(self, x, context=None):
      # note: if no context is given, cross-attention defaults to self-attention
      b, c, h, w = x.shape
      x_in = x
      x = self.norm(x)
      x = self.proj_in(x)
      x = x.transpose([0, 2, 3, 1]).reshape([b, h * w, c])
      for block in self.transformer_blocks:
          x = block(x, context=context)
      x = x.reshape([b, h, w, c]).transpose([0, 3, 1, 2])
      x = self.proj_out(x)
      return x + x_in
  
imports: import math
  from inspect import isfunction
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
label_desc: Use image node_id 2 for calling the SpatialTransformer obj's underlying member method code with example usage: obj.forward(x, context) and returns: unknown