|---| | [ View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.21.0/tensorflow/python/framework/dtypes.py#L51-L288) | Represents the type of the elements in a `Tensor`. Inherits From: [`TraceType`](https://www.tensorflow.org/api_docs/python/tf/types/experimental/TraceType) #### View aliases **Main aliases** [`tf.DType`](https://www.tensorflow.org/api_docs/python/tf/dtypes/DType) **Compat aliases for migration** See [Migration guide](https://www.tensorflow.org/guide/migrate) for more details. [`tf.compat.v1.DType`](https://www.tensorflow.org/api_docs/python/tf/dtypes/DType), [`tf.compat.v1.dtypes.DType`](https://www.tensorflow.org/api_docs/python/tf/dtypes/DType)
tf.dtypes.DType( type_enum, handle_data=None ) `DType`'s are used to specify the output data type for operations which require it, or to inspect the data type of existing `Tensor`'s. #### Examples: tf.constant(1, dtype=tf.int64) tf.constant(1.0).dtype tf.float32 See [`tf.dtypes`](https://www.tensorflow.org/api_docs/python/tf/dtypes) for a complete list of `DType`'s defined.


| ## Attributes || |---|---| | `as_datatype_enum` | Returns a `types_pb2.DataType` enum value based on this data type. | | `as_numpy_dtype` | Returns a Python `type` object based on this `DType`. | | `base_dtype` | Returns a non-reference `DType` based on this `DType` (for TF1).
Programs written for TensorFlow 2.x do not need this attribute. It exists only for compatibility with TensorFlow 1.x, which used reference `DType`s in the implementation of [`tf.compat.v1.Variable`](https://www.tensorflow.org/api_docs/python/tf/compat/v1/Variable). In TensorFlow 2.x, [`tf.Variable`](https://www.tensorflow.org/api_docs/python/tf/Variable) is implemented without reference types. | | `is_bool` | Returns whether this is a boolean data type. | | `is_complex` | Returns whether this is a complex floating point type. | | `is_floating` | Returns whether this is a (non-quantized, real) floating point type. | | `is_integer` | Returns whether this is a (non-quantized) integer type. | | `is_numeric` | Returns whether this is a numeric data type. | | `is_numpy_compatible` | Returns whether this data type has a compatible NumPy data type. | | `is_quantized` | Returns whether this is a quantized data type. | | `is_unsigned` | Returns whether this type is unsigned.
Non-numeric, unordered, and quantized types are not considered unsigned, and this function returns `False`. | | `limits` | Return intensity limits, i.e.
(min, max) tuple, of the dtype. Args: clip_negative : bool, optional If True, clip the negative range (i.e. return 0 for min intensity) even if the image dtype allows negative values. Returns min, max : tuple Lower and upper intensity limits. | | `max` | Returns the maximum representable value in this data type. | | `min` | Returns the minimum representable value in this data type. | | `name` |

| | `real_dtype` | Returns the `DType` corresponding to this `DType`'s real part. | | `size` |

|
## Methods ### `experimental_as_proto` [View source](https://github.com/tensorflow/tensorflow/blob/v2.21.0/tensorflow/python/framework/dtypes.py#L260-L262) experimental_as_proto() -> types_pb2.SerializedDType Returns a proto representation of the Dtype instance. ### `experimental_from_proto` [View source](https://github.com/tensorflow/tensorflow/blob/v2.21.0/tensorflow/python/framework/dtypes.py#L255-L258) @classmethod experimental_from_proto( proto: types_pb2.SerializedDType ) -> 'DType' Returns a Dtype instance based on the serialized proto. ### `experimental_type_proto` [View source](https://github.com/tensorflow/tensorflow/blob/v2.21.0/tensorflow/python/framework/dtypes.py#L250-L253) @classmethod experimental_type_proto() -> Type[types_pb2.SerializedDType] Returns the type of proto associated with DType serialization. ### `is_compatible_with` [View source](https://github.com/tensorflow/tensorflow/blob/v2.21.0/tensorflow/python/framework/dtypes.py#L194-L214) is_compatible_with( other ) Returns True if the `other` DType will be converted to this DType (TF1). Programs written for TensorFlow 2.x do not need this function. Instead, they can do equality comparison on `DType` objects directly: `tf.as_dtype(this) == tf.as_dtype(other)`. This function exists only for compatibility with TensorFlow 1.x, where it additionally allows conversion from a reference type (used by [`tf.compat.v1.Variable`](https://www.tensorflow.org/api_docs/python/tf/compat/v1/Variable)) to its base type.


| 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`. ||
### `is_subtype_of` [View source](https://github.com/tensorflow/tensorflow/blob/v2.21.0/tensorflow/python/framework/dtypes.py#L216-L218) is_subtype_of( other: https://www.tensorflow.org/api_docs/python/tf/types/experimental/TraceType ) -> bool See tf.types.experimental.TraceType base class. ### `most_specific_common_supertype` [View source](https://github.com/tensorflow/tensorflow/blob/v2.21.0/tensorflow/python/framework/dtypes.py#L220-L223) most_specific_common_supertype( types: Sequence[https://www.tensorflow.org/api_docs/python/tf/types/experimental/TraceType] ) -> Optional['DType'] See tf.types.experimental.TraceType base class. ### `__eq__` [View source](https://github.com/tensorflow/tensorflow/blob/v2.21.0/tensorflow/python/framework/dtypes.py#L264-L275) __eq__( other ) Returns True iff this DType refers to the same type as `other`. ### `__ne__` [View source](https://github.com/tensorflow/tensorflow/blob/v2.21.0/tensorflow/python/framework/dtypes.py#L277-L279) __ne__( other ) Returns True iff self != other.