code: def prepare_config_and_inputs(
      self,
      gradient_checkpointing=False,
      scale_attn_by_inverse_layer_idx=False,
      reorder_and_upcast_attn=False,
  ):
      input_ids = ids_tensor(
          [self.batch_size, self.seq_length], self.vocab_size
      ).clamp(3)
  
      input_mask = None
      if self.use_input_mask:
          input_mask = random_attention_mask(
              [self.batch_size, self.seq_length]
          )
  
      config = self.get_config(
          gradient_checkpointing=gradient_checkpointing
      )
  
      head_mask = ids_tensor(
          [self.num_hidden_layers, self.num_attention_heads], 2
      )
  
      return (
          config,
          input_ids,
          input_mask,
          head_mask,
      )
  
imports: import datetime
  import math
  import unittest
  from transformers import XGLMConfig, is_torch_available
  from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
  from ...generation.test_generation_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
label_desc: Use image node_id 3 for calling the XGLMModelTester obj's underlying member method code with example usage: obj.prepare_config_and_inputs(gradient_checkpointing, scale_attn_by_inverse_layer_idx, reorder_and_upcast_attn) and returns: config,  input_ids,  input_mask,  head_mask