code: def output_seq_1():
      class MockTokenizer:
          def decode(self, i=None):
              return ""
  
          def convert_ids_to_tokens(self, i=None):
              return [""]
  
      output_1 = output.OutputSeq(
          **{
              "model_type": "causal",
              "tokenizer": MockTokenizer(),
              "token_ids": [[352, 11, 352, 11, 362]],
              "n_input_tokens": 4,
              "output_text": " 1, 1, 2",
              "tokens": [[" 1", ",", " 1", ",", " 2"]],
              "decoder_hidden_states": [torch.rand(6, 1, 768)],
              "attention": None,
              "attribution": {
                  "gradient": [
                      np.array(
                          [
                              0.41861308,
                              0.13054065,
                              0.23851791,
                              0.21232839,
                          ],
                          dtype=np.float32,
                      )
                  ],
                  "grad_x_input": [
                      np.array(
                          [
                              0.31678662,
                              0.18056837,
                              0.37555906,
                              0.12708597,
                          ],
                          dtype=np.float32,
                      )
                  ],
              },
              "activations": [
                  {
                      "decoder": {
                          "layer_0": torch.rand(1, 768),
                          "layer_1": torch.rand(1, 768),
                          "layer_2": torch.rand(1, 768),
                      }
                  }
              ],
              "lm_head": torch.nn.Linear(768, 50257, bias=False),
              "config": {
                  "embedding": "embeddings.word_embeddings",
                  "type": "mlm",
                  "activations": [
                      "intermediate\.dense"
                  ],  # This is a regex
                  "token_prefix": "▁",
                  "partial_token_prefix": "",
                  "tokenizer_config": {
                      "token_prefix": "▁",
                      "partial_token_prefix": "",
                  },
              },
              "device": "cpu",
          }
      )
  
      yield output_1
  
imports: from ecco import output
  import pytest
  import torch
  import numpy
  from ecco.output import NMF
  import ecco
label_desc: Use image node_id 1 for calling a global function with example usage: output_seq_1() and returns: str, list