class_docstr: A UNetModel that conditions on text with an encoding transformer.
  
  Expects an extra kwarg `tokens` of text.
  
  :param text_ctx: number of text tokens to expect.
  :param xf_width: width of the transformer.
  :param xf_layers: depth of the transformer.
  :param xf_heads: heads in the transformer.
  :param xf_final_ln: use a LayerNorm after the output layer.
  :param tokenizer: the text tokenizer for sampling/vocab size.
code: def del_cache(self):
      self.cache = None
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from .nn import timestep_embedding
  from .unet import UNetModel
  from .xf import LayerNorm, Transformer, convert_module_to_f16
label_desc: Use image node_id 4 for calling the Text2ImUNet obj's underlying member method code with example usage: obj.del_cache() without return types