class_docstr: Displays metadata and associated file info in human-readable format.
class_docstr_tok: ["Displays","metadata","and","associated","file","info","in","human-readable","format","."]
docstr: Creates a MetadataDisplayer object for a file buffer.
  
  Args:
    model_buffer: TensorFlow Lite model buffer in bytearray.
  
  Returns:
    MetadataDisplayer object.
docstr_tok: ["Creates","a","MetadataDisplayer","object","for","a","file","buffer",".","Args",":","model_buffer",":","TensorFlow","Lite","model","buffer","in","bytearray",".","Returns",":","MetadataDisplayer","object","."]
code: def with_model_buffer(cls, model_buffer):
      """Creates a MetadataDisplayer object for a file buffer.
  
      Args:
        model_buffer: TensorFlow Lite model buffer in bytearray.
  
      Returns:
        MetadataDisplayer object.
      """
      if not model_buffer:
          raise ValueError("model_buffer cannot be empty.")
      metadata_buffer = get_metadata_buffer(model_buffer)
      if not metadata_buffer:
          raise ValueError("The model does not have metadata.")
      associated_file_list = cls._parse_packed_associated_file_list(
          model_buffer
      )
      return cls(model_buffer, metadata_buffer, associated_file_list)
  
imports: import copy
  import inspect
  import io
  import json
  import logging
  import os
  import shutil
  import sys
  import tempfile
  from typing import Dict, Optional
  import warnings
  import zipfile
  import flatbuffers
  from mediapipe.tasks.cc.metadata.python import _pywrap_metadata_version
  from mediapipe.tasks.metadata import metadata_schema_py_generated
  from mediapipe.tasks.metadata import schema_py_generated
  from mediapipe.tasks.python.metadata.flatbuffers_lib import _pywrap_flatbuffers
label_desc: Use image node_id 3 for calling the MetadataDisplayer obj's underlying member method code with example usage: obj.with_model_buffer(cls, model_buffer) and returns: cls