code: def test_loss_computation(self):
      (
          config,
          inputs_dict,
      ) = self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes:
          model = model_class(config)
          if getattr(model, "hf_compute_loss", None):
              # The number of elements in the loss should be the same as the number of elements in the label
              prepared_for_class = self._prepare_for_class(
                  inputs_dict.copy(), model_class, return_labels=True
              )
              added_label = prepared_for_class[
                  sorted(
                      list(
                          prepared_for_class.keys() - inputs_dict.keys()
                      ),
                      reverse=True,
                  )[0]
              ]
              expected_loss_size = added_label.shape.as_list()[:1]
  
              # Test that model correctly compute the loss with kwargs
              prepared_for_class = self._prepare_for_class(
                  inputs_dict.copy(), model_class, return_labels=True
              )
              input_ids = prepared_for_class.pop("input_ids")
  
              loss = model(input_ids, **prepared_for_class)[0]
              self.assertTrue(
                  loss.shape.as_list() == expected_loss_size
                  or loss.shape.as_list() == [1]
              )
  
              # Test that model correctly compute the loss when we mask some positions
              prepared_for_class = self._prepare_for_class(
                  inputs_dict.copy(), model_class, return_labels=True
              )
              input_ids = prepared_for_class.pop("input_ids")
              if "labels" in prepared_for_class:
                  labels = prepared_for_class["labels"].numpy()
                  if len(labels.shape) > 1 and labels.shape[1] != 1:
                      labels[0] = -100
                      prepared_for_class[
                          "labels"
                      ] = tf.convert_to_tensor(labels)
                      loss = model(input_ids, **prepared_for_class)[0]
                      self.assertTrue(
                          loss.shape.as_list() == expected_loss_size
                          or loss.shape.as_list() == [1]
                      )
                      self.assertTrue(
                          not np.any(np.isnan(loss.numpy()))
                      )
  
              # Test that model correctly compute the loss with a dict
              prepared_for_class = self._prepare_for_class(
                  inputs_dict.copy(), model_class, return_labels=True
              )
              loss = model(prepared_for_class)[0]
              self.assertTrue(
                  loss.shape.as_list() == expected_loss_size
                  or loss.shape.as_list() == [1]
              )
  
              # Test that model correctly compute the loss with a tuple
              prepared_for_class = self._prepare_for_class(
                  inputs_dict.copy(), model_class, return_labels=True
              )
  
              # Get keys that were added with the _prepare_for_class function
              label_keys = (
                  prepared_for_class.keys() - inputs_dict.keys()
              )
              signature = inspect.signature(model.call).parameters
              signature_names = list(signature.keys())
  
              # Create a dictionary holding the location of the tensors in the tuple
              tuple_index_mapping = {0: "input_ids"}
              for label_key in label_keys:
                  label_key_index = signature_names.index(label_key)
                  tuple_index_mapping[label_key_index] = label_key
              sorted_tuple_index_mapping = sorted(
                  tuple_index_mapping.items()
              )
              # Initialize a list with their default values, update the values and convert to a tuple
              list_input = []
  
              for name in signature_names:
                  if name != "kwargs":
                      list_input.append(signature[name].default)
  
              for index, value in sorted_tuple_index_mapping:
                  list_input[index] = prepared_for_class[value]
  
              tuple_input = tuple(list_input)
  
              # Send to model
              loss = model(tuple_input[:-1])[0]
  
              self.assertTrue(
                  loss.shape.as_list() == expected_loss_size
                  or loss.shape.as_list() == [1]
              )
  
imports: import copy
  import inspect
  import unittest
  import numpy
  from transformers import is_tf_available, is_vision_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_tf, slow
  from transformers.utils import cached_property
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
label_desc: Use image node_id 4 for calling the TFLayoutLMv3ModelTest obj's underlying member method code with example usage: obj.test_loss_computation() without return types