class_docstr: Deserialize CSV or JSON data from an inference endpoint into a pandas dataframe.
class_docstr_tok: ["Deserialize","CSV","or","JSON","data","from","an","inference","endpoint","into","a","pandas","dataframe","."]
docstr: Initialize a ``PandasDeserializer`` instance.
  
  Args:
      accept (union[str, tuple[str]]): The MIME type (or tuple of allowable MIME types) that
          is expected from the inference endpoint (default: ("text/csv","application/json")).
docstr_tok: ["Initialize","a","``","PandasDeserializer","``","instance",".","Args",":","accept","(","union","[","str",",","tuple","[","str","]","]",")",":","The","MIME","type","(","or","tuple","of","allowable","MIME","types",")","that","is","expected","from","the","inference","endpoint","(","default",":","(","``","text\/csv","''",",","''","application\/json","''",")",")","."]
code: def __init__(self, accept=("text/csv", "application/json")):
      """Initialize a ``PandasDeserializer`` instance.
  
      Args:
          accept (union[str, tuple[str]]): The MIME type (or tuple of allowable MIME types) that
              is expected from the inference endpoint (default: ("text/csv","application/json")).
      """
      super(PandasDeserializer, self).__init__(accept=accept)
  
imports: from __future__ import absolute_import
  import csv
  import abc
  import codecs
  import io
  import json
  import numpy
  from six import with_metaclass
  from sagemaker.utils import DeferredError
label_desc: Use image node_id 1 to create a new PandasDeserializer object from inherited base classes: SimpleBaseDeserializer with example: obj = PandasDeserializer(accept)