class_docstr: Language model head that shares the same parameters with the embedding matrix.
class_docstr_tok: ["Language","model","head","that","shares","the","same","parameters","with","the","embedding","matrix","."]
code: def __init__(
      self, embed=None, vocab_size=None, dtype=None, embed_dim=None
  ):
      super().__init__()
      if embed is not None:
          self.head = embed
      else:
          self.head = VocabParallelEmbedding1D(
              vocab_size, embed_dim, dtype=dtype
          )
  
imports: import torch
  import torch.nn.init
  from torch import Tensor
  from torch import nn
  from torch.nn import functional
  from torch.nn.parameter import Parameter
  from colossalai.legacy.context import ParallelMode, seed
  from colossalai.legacy.core import global_context
  from colossalai.legacy.nn.layer.base_layer import ParallelLayer
  from colossalai.legacy.nn.layer.parallel_1d._utils import gather_forward_split_backward, reduce_grad, reduce_input
  from colossalai.legacy.nn.layer.parallel_1d.layers import Linear1D_Row
  from colossalai.legacy.nn.layer.utils import divide
  from colossalai.legacy.registry import LAYERS, LOSSES
  from colossalai.utils import get_current_device
label_desc: Use image node_id 1 to create a new VocabParallelGPTLMHead1D object from inherited base classes: ParallelLayer with example: obj = VocabParallelGPTLMHead1D(embed, vocab_size, dtype, embed_dim)