class_docstr: Transformer language model.
  
  Arguments:
      transformer_hparams: transformer hyperparameters
      vocab_size: vocabulary size
      max_sequence_length: maximum size of sequence. This
                           is used for positional embedding
      embedding_dropout_prob: dropout probability for embeddings
      num_tokentypes: size of the token-type embeddings. 0 value
                      will ignore this embedding
docstr: See megatron.model.transformer.set_input_tensor()
docstr_tok: ["See","megatron.model.transformer.set_input_tensor","(",")"]
code: def set_input_tensor(self, input_tensor):
      """See megatron.model.transformer.set_input_tensor()"""
      # This is usually handled in schedules.py but some inference code still
      # gives us non-lists or None
      if not isinstance(input_tensor, list):
          input_tensor = [input_tensor]
  
      self.encoder.set_input_tensor(input_tensor[0])
  
imports: from ast import Mod
  import torch
  from nemo.collections.nlp.modules.common.megatron.adapters.parallel_adapters import AdapterName, PromptEncoderAdapterConfig
  from nemo.collections.nlp.modules.common.megatron.layer_type import LayerType
  from nemo.collections.nlp.modules.common.megatron.module import MegatronModule
  from nemo.collections.nlp.modules.common.megatron.position_embedding import ALiBiRelativePositionEmbedding, KERPLERelativePositionEmbedding, RotaryEmbedding, SandwichRelativePositionEmbedding
  from nemo.collections.nlp.modules.common.megatron.transformer import ParallelTransformer
  from nemo.collections.nlp.modules.common.megatron.utils import ApexGuardDefaults, get_linear_layer, init_method_normal, scaled_init_method_normal
  from nemo.collections.nlp.parts import utils_funcs
  from nemo.core import adapter_mixins
label_desc: Use image node_id 2 for calling the TransformerLanguageModel obj's underlying member method code with example usage: obj.set_input_tensor(input_tensor) without return types