code: def forward(self, encoder_input_tokens, encoder_inputs_mask):
      x = self.token_embedder(encoder_input_tokens)
  
      seq_length = encoder_input_tokens.shape[1]
      inputs_positions = torch.arange(
          seq_length, device=encoder_input_tokens.device
      )
      x += self.position_encoding(inputs_positions)
  
      x = self.dropout_pre(x)
  
      # inverted the attention mask
      input_shape = encoder_input_tokens.size()
      extended_attention_mask = self.get_extended_attention_mask(
          encoder_inputs_mask, input_shape
      )
  
      for lyr in self.encoders:
          x = lyr(x, extended_attention_mask)[0]
      x = self.layer_norm(x)
  
      return self.dropout_post(x), encoder_inputs_mask
  
file_path: diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/notes_encoder.py
imports: import torch
  import torch.nn
  from transformers.modeling_utils import ModuleUtilsMixin
  from transformers.models.t5.modeling_t5 import T5Block, T5Config, T5LayerNorm
  from ....configuration_utils import ConfigMixin, register_to_config
  from ....models import ModelMixin
label_desc: Use image node_id 2 for calling the SpectrogramNotesEncoder obj's underlying member method code with example usage: obj.forward(encoder_input_tokens, encoder_inputs_mask) and returns: self,  encoder_inputs_mask