code: def prepare_time_series_transformer_inputs_dict(self, config):
      _past_length = config.context_length + max(config.lags_sequence)
  
      static_categorical_features = ids_tensor(
          [self.batch_size, 1], config.cardinality[0]
      )
      static_real_features = floats_tensor([self.batch_size, 1])
  
      past_time_features = floats_tensor(
          [self.batch_size, _past_length, config.num_time_features]
      )
      past_values = floats_tensor([self.batch_size, _past_length])
      past_observed_mask = floats_tensor(
          [self.batch_size, _past_length]
      )
  
      # decoder inputs
      future_time_features = floats_tensor(
          [
              self.batch_size,
              config.prediction_length,
              config.num_time_features,
          ]
      )
      future_values = floats_tensor(
          [self.batch_size, config.prediction_length]
      )
  
      inputs_dict = {
          "past_values": past_values,
          "static_categorical_features": static_categorical_features,
          "static_real_features": static_real_features,
          "past_time_features": past_time_features,
          "past_observed_mask": past_observed_mask,
          "future_time_features": future_time_features,
          "future_values": future_values,
      }
      return inputs_dict
  
imports: import inspect
  import tempfile
  import unittest
  from huggingface_hub import hf_hub_download
  from transformers import is_torch_available
  from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
label_desc: Use image node_id 3 for calling the TimeSeriesTransformerModelTester obj's underlying member method code with example usage: obj.prepare_time_series_transformer_inputs_dict(config) and returns: inputs_dict