docstr: # Args.
      max_input_length: int, maximum input context length.
      step: int, the current step index.
      ite: int, local batch iteration.
      input_embeds: Tensor, (local_batch * beam, hidden_dim),
          input hidden state to decoder.
      sequence_lengths: IntTensor, (local_batch * beam,),
          the current sequence lengths.
      key_cache: Tensor, key cache buffer.
      value_cache: Tensor, value cache buffer.
      finished: BoolTensor, (local_batch * beam,),
          whether to finish sentence generation.
      total_padding_tokens IntTensor, (local_batch * beam,),
          the number of padded tokens.
      masked_tokens: BoolTensor, (local_batch * beam, memory_length),
          a mask tensor that indicates padded tokens.
      cache_indirection: IntTensor, (local_batch * beam,),
          cache of beam positions if needed if beam > 1.
      linear_bias_slopes Tensor, (num_heads,)
          slopes head of linear position bias (ALiBi) (optional).
  # Returns
      IntTensor, (batch * beam,) output token ids.
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code: def forward(
      self,
      max_input_length: int,
      step: int,
      ite: int,
      input_embeds: torch.Tensor,
      sequence_lengths: torch.IntTensor,
      key_cache: torch.Tensor,
      value_cache: torch.Tensor,
      finished: torch.BoolTensor,
      total_padding_tokens: torch.IntTensor,
      masked_tokens: torch.BoolTensor,
      cache_indirection: Optional[torch.IntTensor] = None,
      linear_bias_slopes: Optional[torch.Tensor] = None,
  ):
      """
  
      # Args.
          max_input_length: int, maximum input context length.
          step: int, the current step index.
          ite: int, local batch iteration.
          input_embeds: Tensor, (local_batch * beam, hidden_dim),
              input hidden state to decoder.
          sequence_lengths: IntTensor, (local_batch * beam,),
              the current sequence lengths.
          key_cache: Tensor, key cache buffer.
          value_cache: Tensor, value cache buffer.
          finished: BoolTensor, (local_batch * beam,),
              whether to finish sentence generation.
          total_padding_tokens IntTensor, (local_batch * beam,),
              the number of padded tokens.
          masked_tokens: BoolTensor, (local_batch * beam, memory_length),
              a mask tensor that indicates padded tokens.
          cache_indirection: IntTensor, (local_batch * beam,),
              cache of beam positions if needed if beam > 1.
          linear_bias_slopes Tensor, (num_heads,)
              slopes head of linear position bias (ALiBi) (optional).
      # Returns
          IntTensor, (batch * beam,) output token ids.
      """
  
      self._initialize_model()
  
      outputs = self.ft_op.forward(
          max_input_length,
          step,
          ite,
          input_embeds,
          sequence_lengths,
          finished,
          total_padding_tokens,
          masked_tokens,
          key_cache,
          value_cache,
          cache_indirection,
          linear_bias_slopes,
      )
      return outputs[0]
  
imports: import math
  from abc import abstractmethod
  from pathlib import Path
  from typing import List, Literal, Optional, Union
  import os
  import numpy
  from .None import comm
  from .None import profiler
  from .gpt import GptInitModelParameters
  from nebullvm.optional_modules.torch import torch
label_desc: Use image node_id 5 for calling the GptDecoder obj's underlying member method code with example usage: obj.forward(max_input_length, step, ite, input_embeds, sequence_lengths, key_cache, value_cache, finished, total_padding_tokens, masked_tokens, cache_indirection, linear_bias_slopes) and returns: outputs