class_docstr: LIT model backed by a remote LIT server.
class_docstr_tok: ["LIT","model","backed","by","a","remote","LIT","server","."]
docstr: Initialize model wrapper from remote server.
  
  Args:
    url: url of LIT server
    name: name of model on the remote server
    max_minibatch_size: batch size used for remote requests
docstr_tok: ["Initialize","model","wrapper","from","remote","server",".","Args",":","url",":","url","of","LIT","server","name",":","name","of","model","on","the","remote","server","max_minibatch_size",":","batch","size","used","for","remote","requests"]
code: def __init__(
      self, url: str, name: str, max_minibatch_size: int = 256
  ):
      """Initialize model wrapper from remote server.
  
      Args:
        url: url of LIT server
        name: name of model on the remote server
        max_minibatch_size: batch size used for remote requests
      """
      self._url = url
      self._name = name
  
      # Get specs
      server_info = query_lit_server(self._url, "get_info")
      model_spec = server_info["models"][self._name]["spec"]
      self._input_spec = model_spec["input"]
      self._output_spec = model_spec["output"]
  
      self._max_minibatch_size = max_minibatch_size
  
file_path: lit/lit_nlp/components/remote_model.py
imports: from typing import Any, Optional
  import urllib
  from absl import logging
  from lit_nlp.api import model
  from lit_nlp.api import types
  from lit_nlp.lib import serialize
  import requests
  import six
label_desc: Use image node_id 1 to create a new RemoteModel object from inherited base classes: lit_model with example: obj = RemoteModel(url, name, max_minibatch_size)