docstr: Get the initial token embeddings that considers the token type and positional embeddings
  
  Parameters
  ----------
  inputs
      - layout = 'NT'
          Shape (batch_size, seq_length)
      - layout = 'TN'
          Shape (seq_length, batch_size)
  
  Returns
  -------
  embedding
      The initial embedding that will be fed into the encoder
  
      - layout = 'NT'
          Shape (batch_size, seq_length, C)
      - layout = 'TN'
          Shape (seq_length, batch_size, C)
docstr_tok: ["Get","the","initial","token","embeddings","that","considers","the","token","type","and","positional","embeddings","Parameters","--","--","--","--","--","inputs","-","layout","=","'NT'","Shape","(","batch_size",",","seq_length",")","-","layout","=","'TN'","Shape","(","seq_length",",","batch_size",")","Returns","--","--","--","-","embedding","The","initial","embedding","that","will","be","fed","into","the","encoder","-","layout","=","'NT'","Shape","(","batch_size",",","seq_length",",","C",")","-","layout","=","'TN'","Shape","(","seq_length",",","batch_size",",","C",")"]
code: def get_initial_embedding(self, inputs):
      """Get the initial token embeddings that considers the token type and positional embeddings
  
      Parameters
      ----------
      inputs
          - layout = 'NT'
              Shape (batch_size, seq_length)
          - layout = 'TN'
              Shape (seq_length, batch_size)
  
      Returns
      -------
      embedding
          The initial embedding that will be fed into the encoder
  
          - layout = 'NT'
              Shape (batch_size, seq_length, C)
          - layout = 'TN'
              Shape (seq_length, batch_size, C)
  
      """
      if self._layout == "NT":
          batch_axis, time_axis = 0, 1
      else:
          batch_axis, time_axis = 1, 0
      embedding = self.word_embed(inputs)
      if self.pos_embed_type:
          positional_embedding = self.pos_embed(
              npx.arange_like(inputs, axis=time_axis)
          )
          positional_embedding = np.expand_dims(
              positional_embedding, axis=batch_axis
          )
          embedding = embedding + positional_embedding
      if self.encoder_normalize_before:
          embedding = self.embed_ln(embedding)
      embedding = self.embed_dropout(embedding)
  
      return embedding
  
imports: import os
  from typing import Tuple
  import mxnet
  from mxnet import use_np, np, npx
  from mxnet.gluon import HybridBlock, nn
  from .base import BACKBONE_REGISTRY
  from ..op import select_vectors_by_position
  from ..base import get_model_zoo_home_dir, get_repo_model_zoo_url, get_model_zoo_checksum_dir
  from ..layers import PositionalEmbedding, get_activation
  from ..utils.misc import download, load_checksum_stats
  from ..utils.registry import Registry
  from .transformer import TransformerEncoderLayer
  from ..initializer import TruncNorm
  from ..utils.config import CfgNode
  from ..attention_cell import gen_self_attn_mask
  from ..data.tokenizers import HuggingFaceByteBPETokenizer
label_desc: Use image node_id 4 for calling the RobertaModel obj's underlying member method code with example usage: obj.get_initial_embedding(inputs) and returns: embedding