class_docstr: Implements an API to store large tensors outside the main ModelProto,
  it avoids copying large initializers when defining the model and these initializers
  are never serialized through protobuf.
  No tensor is stored on disk until the user explicitly saves the model.
class_docstr_tok: ["Implements","an","API","to","store","large","tensors","outside","the","main","ModelProto",",","it","avoids","copying","large","initializers","when","defining","the","model","and","these","initializers","are","never","serialized","through","protobuf",".","No","tensor","is","stored","on","disk","until","the","user","explicitly","saves","the","model","."]
docstr: Tells if an initializer name is an external initializer stored in memory.
  The name must start with '#' in that case.
docstr_tok: ["Tells","if","an","initializer","name","is","an","external","initializer","stored","in","memory",".","The","name","must","start","with","'","#","'","in","that","case","."]
code: def is_in_memory_external_initializer(self, name: str) -> bool:
      """Tells if an initializer name is an external initializer stored in memory.
      The name must start with '#' in that case.
      """
      return name.startswith("#")
  
imports: from __future__ import annotations
  import os
  import sys
  from typing import Any, Iterable
  import numpy
  import onnx
  import onnx.external_data_helper
  import onnx.helper
label_desc: Use image node_id 7 for calling the ModelContainer obj's underlying member method code with example usage: obj.is_in_memory_external_initializer(name) and returns: name