class_docstr: Represents the type of the elements in a `Tensor`.
  
  The following `DType` objects are defined:
  
  * `tf.float16`: 16-bit half-precision floating-point.
  * `tf.float32`: 32-bit single-precision floating-point.
  * `tf.float64`: 64-bit double-precision floating-point.
  * `tf.bfloat16`: 16-bit truncated floating-point.
  * `tf.complex64`: 64-bit single-precision complex.
  * `tf.complex128`: 128-bit double-precision complex.
  * `tf.int8`: 8-bit signed integer.
  * `tf.uint8`: 8-bit unsigned integer.
  * `tf.uint16`: 16-bit unsigned integer.
  * `tf.uint32`: 32-bit unsigned integer.
  * `tf.uint64`: 64-bit unsigned integer.
  * `tf.int16`: 16-bit signed integer.
  * `tf.int32`: 32-bit signed integer.
  * `tf.int64`: 64-bit signed integer.
  * `tf.bool`: Boolean.
  * `tf.string`: String.
  * `tf.qint8`: Quantized 8-bit signed integer.
  * `tf.quint8`: Quantized 8-bit unsigned integer.
  * `tf.qint16`: Quantized 16-bit signed integer.
  * `tf.quint16`: Quantized 16-bit unsigned integer.
  * `tf.qint32`: Quantized 32-bit signed integer.
  * `tf.resource`: Handle to a mutable resource.
  * `tf.variant`: Values of arbitrary types.
  
  In addition, variants of these types with the `_ref` suffix are
  defined for reference-typed tensors.
  
  The `tf.as_dtype()` function converts numpy types and string type
  names to a `DType` object.
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code: def __repr__(self):
      return "tf." + self.name
  
imports: import numpy
  from .None import pywrap_tensorflow
  from tensorboard.compat.proto import types_pb2
label_desc: Use image node_id 24 for calling the DType obj's underlying member method code with example usage: obj.__repr__() and returns: unknown