code: def forward(self, *inputs, **kwargs):
      # Note: Legacy checkpoints didn't have pre-process.
      if getattr(self.module, "pre_process", True):
          inputs = fp32_to_float16(inputs, self.float16_converter)
      outputs = self.module(*inputs, **kwargs)
      if parallel_state.is_pipeline_last_stage() and self.training:
          outputs = float16_to_fp32(outputs)
      return outputs
  
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 3 for calling the Float16Module obj's underlying member method code with example usage: obj.forward() and returns: outputs