class_docstr: Deserialize a stream of data in .npy, .npz or UTF-8 CSV/JSON format to a numpy array.
  
  Note that when using application/x-npz archive format, the result will usually be a
  dictionary-like object containing multiple arrays (as per ``numpy.load()``) - instead of a
  single array.
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docstr: Initialize a ``NumpyDeserializer`` instance.
  
  Args:
      dtype (str): The dtype of the data (default: None).
      accept (union[str, tuple[str]]): The MIME type (or tuple of allowable MIME types) that
          is expected from the inference endpoint (default: "application/x-npy").
      allow_pickle (bool): Allow loading pickled object arrays (default: True).
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code: def __init__(
      self, dtype=None, accept="application/x-npy", allow_pickle=True
  ):
      """Initialize a ``NumpyDeserializer`` instance.
  
      Args:
          dtype (str): The dtype of the data (default: None).
          accept (union[str, tuple[str]]): The MIME type (or tuple of allowable MIME types) that
              is expected from the inference endpoint (default: "application/x-npy").
          allow_pickle (bool): Allow loading pickled object arrays (default: True).
      """
      super(NumpyDeserializer, self).__init__(accept=accept)
      self.dtype = dtype
      self.allow_pickle = allow_pickle
  
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 NumpyDeserializer object from inherited base classes: SimpleBaseDeserializer with example: obj = NumpyDeserializer(dtype, accept, allow_pickle)