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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docstr: Returns True if the `other` DType will be converted to this DType.
  
  The conversion rules are as follows:
  
  ```python
  DType(T)       .is_compatible_with(DType(T))        == True
  DType(T)       .is_compatible_with(DType(T).as_ref) == True
  DType(T).as_ref.is_compatible_with(DType(T))        == False
  DType(T).as_ref.is_compatible_with(DType(T).as_ref) == True
  ```
  
  Args:
    other: A `DType` (or object that may be converted to a `DType`).
  
  Returns:
    True if a Tensor of the `other` `DType` will be implicitly converted to
    this `DType`.
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code: def is_compatible_with(self, other):
      """Returns True if the `other` DType will be converted to this DType.
  
      The conversion rules are as follows:
  
      ```python
      DType(T)       .is_compatible_with(DType(T))        == True
      DType(T)       .is_compatible_with(DType(T).as_ref) == True
      DType(T).as_ref.is_compatible_with(DType(T))        == False
      DType(T).as_ref.is_compatible_with(DType(T).as_ref) == True
      ```
  
      Args:
        other: A `DType` (or object that may be converted to a `DType`).
  
      Returns:
        True if a Tensor of the `other` `DType` will be implicitly converted to
        this `DType`.
      """
      other = as_dtype(other)
      return self._type_enum in (
          other.as_datatype_enum,
          other.base_dtype.as_datatype_enum,
      )
  
imports: import numpy
  from .None import pywrap_tensorflow
  from tensorboard.compat.proto import types_pb2
label_desc: Use image node_id 18 for calling the DType obj's underlying member method code with example usage: obj.is_compatible_with(other) and returns: unknown