class_docstr: Generic Metadata writer.
  
  Example usage:
  
  For an example model which requires two inputs: image and general feature
  inputs, and generates one output: classification.
  
  with open(model_path, 'rb') as f:
    writer = MetadataWriter.create(f.read())
    model_content, metadata_json_content = writer
        .add_genernal_info('model_name', 'model description')
        .add_image_input()
        .add_feature_input()
        .add_classification_output(Labels().add(['A', 'B']))
        .populate()
class_docstr_tok: ["Generic","Metadata","writer",".","Example","usage",":","For","an","example","model","which","requires","two","inputs",":","image","and","general","feature","inputs",",","and","generates","one","output",":","classification",".","with","open","(","model_path",",","'rb","'",")","as","f",":","writer","=","MetadataWriter.create","(","f.read","(",")",")","model_content",",","metadata_json_content","=","writer",".add_genernal_info","(","'model_name","'",",","'model","description","'",")",".add_image_input","(",")",".add_feature_input","(",")",".add_classification_output","(","Labels","(",")",".add","(","[","'","A","'",",","'","B","'","]",")",")",".populate","(",")"]
code: def create(cls, model_buffer: bytearray) -> "MetadataWriter":
      return cls(model_buffer)
  
imports: import collections
  import csv
  import dataclasses
  import os
  import tempfile
  from typing import List, Optional, Tuple, Union
  import flatbuffers
  from mediapipe.tasks.metadata import metadata_schema_py_generated
  from mediapipe.tasks.python.metadata import metadata
  from mediapipe.tasks.python.metadata.metadata_writers import metadata_info
  from mediapipe.tasks.python.metadata.metadata_writers import writer_utils
label_desc: Use image node_id 1 for calling the MetadataWriter obj's underlying member method code with example usage: obj.create(cls, model_buffer) and returns: cls