code: def forward(
      self, batch: FlashMistralBatch
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      # Model Forward
      if batch.speculative_ids is not None:
          input_ids = batch.input_ids
          position_ids = batch.position_ids
          cu_seqlen_prefill = batch.cu_seqlen_prefill
          kv_cache = get_cache_manager().kv_cache
          block_tables = batch.block_tables_tensor
          slots = batch.slots[batch.slot_indices]
          input_lengths = batch.input_lengths_tensor
          max_s = batch.max_seqlen
          lm_head_indices = batch.prefill_head_indices
  
          speculative_ids = batch.speculative_ids
  
          B, speculative_length = speculative_ids.shape
          new_length = speculative_length + 1
          new_input_ids = torch.cat(
              [input_ids.unsqueeze(-1), speculative_ids], dim=1
          ).reshape(-1)
          arange = torch.arange(
              new_length, device=position_ids.device
          ).unsqueeze(0)
          arange_int = arange.to(dtype=torch.int32)
          new_position_ids = (
              position_ids.unsqueeze(-1).expand(B, new_length) + arange
          ).view(-1)
          slots = (
              slots.unsqueeze(-1).expand(B, new_length) + arange_int
          ).view(-1)
          input_lengths = (
              input_lengths.unsqueeze(-1).expand(B, new_length)
              + arange_int
          ).view(-1)
  
          # Add Copy the block tables for all members
          block_tables = (
              block_tables.unsqueeze(1)
              .expand(B, new_length, -1)
              .reshape(B * new_length, -1)
              .contiguous()
          )
          max_s = max_s + speculative_length
  
          input_ids = new_input_ids
          position_ids = new_position_ids
      else:
          input_ids = batch.input_ids
          position_ids = batch.position_ids
          cu_seqlen_prefill = batch.cu_seqlen_prefill
          kv_cache = get_cache_manager().kv_cache
          block_tables = batch.block_tables_tensor
          slots = batch.slots[batch.slot_indices]
          input_lengths = batch.input_lengths_tensor
          max_s = batch.max_seqlen
          lm_head_indices = batch.prefill_head_indices
      logits = self.model.forward(
          input_ids=input_ids,
          position_ids=position_ids,
          cu_seqlen_prefill=cu_seqlen_prefill,
          kv_cache=kv_cache,
          block_tables=block_tables,
          slots=slots,
          input_lengths=input_lengths,
          max_s=max_s,
          prefill_cache_indices=batch.prefill_cache_indices,
          lm_head_indices=lm_head_indices,
      )
      if batch.prefill_cache_indices is not None:
          batch.prefill_cache_indices = None
      return logits
  
imports: import math
  import torch
  import torch.distributed
  import numpy
  from dataclasses import dataclass
  from opentelemetry import trace
  from transformers import PreTrainedTokenizerBase
  from transformers.models.llama import LlamaTokenizerFast
  from typing import Optional, Tuple, Type, List
  from text_generation_server.pb import generate_pb2
  from text_generation_server.models import FlashCausalLM
  from text_generation_server.models.flash_causal_lm import FlashCausalLMBatch, BLOCK_SIZE
  from text_generation_server.models.cache_manager import get_cache_manager
  from text_generation_server.models.custom_modeling.flash_mistral_modeling import FlashMistralForCausalLM, MistralConfig
  from text_generation_server.utils.speculate import get_speculate
  from text_generation_server.utils import initialize_torch_distributed, weight_files, Weights, HeterogeneousNextTokenChooser, StoppingCriteria
label_desc: Use image node_id 3 for calling the BaseFlashMistral obj's underlying member method code with example usage: obj.forward(batch) and returns: logits