class_docstr: Metadata for input sequences. Used for PagedAttention.
  
  Args:
      seq_groups: List of (seq_ids, sampling_params).
      seq_data: Seq_id -> SequenceData.
      prompt_lens: Lengths of prompts.
      slot_mapping: The address to write the new KV to of each token.
      context_lens: the length of attention context for each generation token.
      max_context_len: The maximum context length.
      block_tables: The block tables. (Seq id -> list of physical block)
code: def __init__(
      self,
      seq_groups: List[Tuple[List[int], SamplingParams]],
      seq_data: Dict[int, SequenceData],
      prompt_lens: List[int],
      context_lens: torch.Tensor,
      max_context_len: int,
      sliding_window: Optional[int] = None,
  ) -> None:
      self.seq_groups = seq_groups
      self.seq_data = seq_data
      self.prompt_lens = prompt_lens
      self.context_lens = context_lens
      self.max_context_len = max_context_len
  
      self.to_cache = None
  
      self.num_prompts = len(prompt_lens)
      self.num_prompt_tokens = sum(prompt_lens)
      self.num_generation_tokens = context_lens.shape[0]
  
imports: from typing import Dict, List, Optional, Tuple
  import torch
  from bigdl.llm.vllm.sequence import SequenceData
  from bigdl.llm.vllm.sampling_params import SamplingParams
label_desc: Use image node_id 1 to create a new InputMetadata object  with example: obj = InputMetadata(seq_groups, seq_data, prompt_lens, context_lens, max_context_len, sliding_window)