class_docstr: Wrapper for the Transformer-Engine's `Linear` layer.
  
  Note that if Megatron's parallel_state has not been initialized
  yet, the tp_group passed to TE will be None and must be set later
  via set_tensor_parallel_group().
code: def forward(self, x):
      out = super().forward(x)
  
      # TE only returns a tuple when return_bias is True, otherwise
      # it returns a single Tensor, we always want to return two
      # values regardless of the arguments.
      if self.te_return_bias:
          return out
      return out, None
  
imports: import os
  from importlib.metadata import version
  from typing import Callable
  import torch
  import transformer_engine
  from pkg_resources import packaging
  from torch import Tensor
  from megatron.core import ModelParallelConfig
  from megatron.core.parallel_state import get_context_parallel_global_ranks, get_context_parallel_group, get_tensor_model_parallel_group
  from megatron.core.tensor_parallel import get_cuda_rng_tracker
  from megatron.core.transformer.enums import AttnMaskType
  from megatron.core.transformer.transformer_config import TransformerConfig
  from megatron.core.transformer.utils import make_sharded_tensors_for_checkpoint
label_desc: Use image node_id 2 for calling the TELinear obj's underlying member method code with example usage: obj.forward(x) and returns: out,  None, out