class_docstr: Combines decoder_embedding with the decoder component
class_docstr_tok: ["Combines","decoder_embedding","with","the","decoder","component"]
code: def input_example(self, max_batch=1, max_dim=768, seq_len=6):
      enc_output = torch.randint(
          low=-3,
          high=3,
          size=(max_batch, seq_len, max_dim),
          device=self.device,
          dtype=torch.float32,
      )
      enc_attn_mask = torch.tensor([[1 for _ in range(seq_len)]]).to(
          self.device
      )
  
      dec_len = random.randint(10, 128)
      dec_input = torch.randint(
          low=0,
          high=1000,
          size=(max_batch, dec_len),
          device=self.device,
      )
      dec_attn_mask = torch.tensor([[1 for _ in range(dec_len)]]).to(
          self.device
      )
  
      # constant decoder_mems as placeholder for now
      decoder_mems = torch.zeros(
          [max_batch, self.decoder.num_layers + 1, seq_len, max_dim],
          dtype=torch.float32,
      ).to(self.device)
  
      # input_ids, decoder_mask, encoder_mask, encoder_embeddings
  
      return tuple(
          [
              dec_input,
              dec_attn_mask,
              enc_attn_mask,
              enc_output,
              decoder_mems,
          ]
      )
  
imports: import random
  from typing import Dict, List, Optional
  import torch
  import torch.nn.functional
  from nemo.collections.nlp.modules.common.megatron.utils import build_position_ids
  from nemo.core.classes.exportable import Exportable
  from nemo.core.neural_types import ChannelType, EncodedRepresentation, MaskType, NeuralType
label_desc: Use image node_id 6 for calling the DecEmb obj's underlying member method code with example usage: obj.input_example(max_batch, max_dim, seq_len) and returns: tuple