class_docstr: Metadata container for an embedding.
  
  The metadata holds different columns with values used for
  visualization (color by, label by) in the "Embeddings" tab in
  TensorBoard.
class_docstr_tok: ["Metadata","container","for","an","embedding",".","The","metadata","holds","different","columns","with","values","used","for","visualization","(","color","by",",","label","by",")","in","the","``","Embeddings","''","tab","in","TensorBoard","."]
docstr: Constructs a metadata for an embedding of the specified size.
  
  Args:
    num_points: Number of points in the embedding.
code: def __init__(self, num_points):
      """Constructs a metadata for an embedding of the specified size.
  
      Args:
        num_points: Number of points in the embedding.
      """
      self.num_points = num_points
      self.column_names = []
      self.name_to_values = {}
  
imports: import collections
  import functools
  import imghdr
  import mimetypes
  import os
  import threading
  import numpy
  from werkzeug import wrappers
  from google.protobuf import json_format
  from google.protobuf import text_format
  from tensorboard import context
  from tensorboard.backend.event_processing import plugin_asset_util
  from tensorboard.backend.http_util import Respond
  from tensorboard.compat import tf
  from tensorboard.plugins import base_plugin
  from tensorboard.plugins.projector import metadata
  from tensorboard.plugins.projector.projector_config_pb2 import ProjectorConfig
  from tensorboard.util import tb_logging
label_desc: Use image node_id 1 to create a new EmbeddingMetadata object  with example: obj = EmbeddingMetadata(num_points)