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 __repr__(self) -> str:
      # Print only useful metadata.
      return (
          f"InputMetadata("
          f"num_valid_tokens={self.num_valid_tokens}, "
          f"num_prompt_tokens={self.num_prompt_tokens}, "
          f"num_prompts={self.num_prompts}, "
          f"prompt_lens={self.prompt_lens}, "
          f"num_generation_tokens={self.num_generation_tokens}, "
          f"context_lens={self.context_lens}, "
          f"max_context_len={self.max_context_len}), "
          f"max_num_blocks_per_seq={self.max_num_blocks_per_seq}, "
          f"block_tables={self.block_tables}), "
          f"slot_mapping={self.slot_mapping}"
      )
  
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 2 for calling the InputMetadata obj's underlying member method code with example usage: obj.__repr__() and returns: str, self, str, self, str, self, str, self, str, self, str, self, str, self, str, self, str, self, str, self