class_docstr: Language model embeddings.
  
  Arguments:
      hidden_size: hidden size
      vocab_size: vocabulary size
      max_sequence_length: maximum size of sequence. This
                           is used for positional embedding
      embedding_dropout_prob: dropout probability for embeddings
      init_method: weight initialization method
      num_tokentypes: size of the token-type embeddings. 0 value
                      will ignore this embedding
      position_embedding_type: position embedding type determines whether we instantiate a learnable position embedding table.
docstr: For easy load.
docstr_tok: ["For","easy","load","."]
code: def state_dict_for_save_checkpoint(
      self, destination=None, prefix="", keep_vars=False
  ):
      """For easy load."""
  
      state_dict_ = {}
      state_dict_[
          self._word_embeddings_key
      ] = self.word_embeddings.state_dict(
          destination, prefix, keep_vars
      )
      if self.position_embedding_type == "learned_absolute":
          state_dict_[
              self._position_embeddings_key
          ] = self.position_embeddings.state_dict(
              destination, prefix, keep_vars
          )
      if self.num_tokentypes > 0:
          state_dict_[
              self._tokentype_embeddings_key
          ] = self.tokentype_embeddings.state_dict(
              destination, prefix, keep_vars
          )
  
      return state_dict_
  
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 5 for calling the Embedding obj's underlying member method code with example usage: obj.state_dict_for_save_checkpoint(destination, prefix, keep_vars) and returns: state_dict_