code: def test_build_module(self):
      # Check NoOp TransformerLayer
      random_input = 12
      noop_transformer_layer = [
          build_module(getattr(self.transformer_layer_spec, field.name))
          for field in fields(self.transformer_layer_spec)
      ]
  
      x = random_input
      for mod in noop_transformer_layer:
          # checking for `IdentityFuncOp` before `IdentityOp` because former
          # is derived from the latter and so the second if statement will
          # always be `True`.
          if isinstance(mod, IdentityFuncOp):
              x = mod()(x)
          elif isinstance(mod, IdentityOp):
              x = mod(x)
  
      assert x == random_input
  
      # Check SelfAttention
      self_attention = build_module(
          self.attention_spec,
          config=self.config,
          layer_number=1,
      )
      assert isinstance(self_attention, SelfAttention)
      assert self_attention.layer_number == 1
      assert (
          self_attention.attn_mask_type
          == self.attention_spec.params["attn_mask_type"]
      )
  
      num_weights = sum(
          [p.numel() for p in self_attention.parameters()]
      )
      assert num_weights == 648
  
      # Check SelfAttention but with already initialized module
      # `self_attention`. In this test, `build_module` acts as a no op as it
      # simply returns the initialized module.
      # NOTE: (sudhakars) Uncomment this test once this feature gets added
      # back.
      # self_attention2 = build_module(
      #     self_attention, config=self.config, spec=self.attention_spec,
      # )
      # assert isinstance(self_attention2, SelfAttention)
      # assert self_attention2.layer_number == 1
      # assert self_attention2.attn_mask_type == self.attention_spec.params['attn_mask_type']
  
      # num_weights = sum([p.numel() for p in self_attention2.parameters()])
      # assert num_weights == 648
  
      # Check LayerNorm
      layernorm = build_module(
          self.layernorm_spec,
          config=self.config,
          hidden_size=self.config.hidden_size,
          eps=self.config.layernorm_epsilon,
      )
      assert isinstance(layernorm, te.pytorch.LayerNorm)
  
      # Check BiasDropoutAdd
      bda_op = build_module(self.bda_spec)
      assert id(bda_op) == id(get_bias_dropout_add)
  
imports: import sys
  from dataclasses import dataclass, fields
  from importlib.metadata import version
  import pytest
  import torch
  import transformer_engine
  from pkg_resources import packaging
  from megatron.core.fusions.fused_bias_dropout import get_bias_dropout_add
  from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
  from megatron.core.transformer.attention import SelfAttention, SelfAttentionSubmodules
  from megatron.core.transformer.custom_layers.transformer_engine import TEDotProductAttention, TELayerNormColumnParallelLinear, TENorm, TERowParallelLinear
  from megatron.core.transformer.dot_product_attention import DotProductAttention
  from megatron.core.transformer.enums import AttnMaskType
  from megatron.core.transformer.identity_op import IdentityFuncOp, IdentityOp
  from megatron.core.transformer.spec_utils import ModuleSpec, build_module, import_module
  from megatron.core.transformer.transformer_config import TransformerConfig
  from megatron.core.transformer.transformer_layer import TransformerLayerSubmodules
  from tests.unit_tests.test_utilities import Utils
label_desc: Use image node_id 4 for calling the TestSpecCustomization obj's underlying member method code with example usage: obj.test_build_module() without return types