code: def __init__(
      self,
      config: ModelParallelConfig,
      module,
      precision,
      share_token_embeddings=True,
  ):
      if not HAVE_MEGATRON_CORE:
          raise ImportError(
              "Megatron-core was not found. Please see the NeMo README for installation instructions: https://github.com/NVIDIA/NeMo#megatron-gpt."
          )
      super().__init__(
          config=config, share_token_embeddings=share_token_embeddings
      )
      self.precision = precision
  
      if precision in ["bf16", "bf16-mixed"]:
          self.add_module("module", module.bfloat16())
  
          def float16_converter(val):
              return val.bfloat16()
  
      elif precision in [16, "16", "16-mixed"]:
          self.add_module("module", module.half())
  
          def float16_converter(val):
              return val.half()
  
      else:
          raise Exception(
              f"precision {precision} is not supported. Float16Module (megatron_amp_O2) supports "
              "only fp16 and bf16."
          )
  
      self.float16_converter = float16_converter
  
imports: import torch
  from torch.autograd import Variable
  from torch.nn.parameter import Parameter
  from nemo.collections.nlp.modules.common.megatron.utils import ApexGuardDefaults
  from nemo.utils import logging
label_desc: Use image node_id 1 to create a new Float16Module object from inherited base classes: MegatronModule with example: obj = Float16Module(config, module, precision, share_token_embeddings)