# TODO(rahulkamat): Add missing types that are convertible to Tensor.
TensorLike = Union[Tensor, TensorProtocol, int, float, bool, str, bytes,
complex, tuple, list, np.ndarray, np.generic]
doc_typealias.document(
obj=TensorLike,
doc=textwrap.dedent("""\
Union of all types that can be converted to a `tf.Tensor` by `tf.convert_to_tensor`.
This definition may be used in user code. Additional types may be added
in the future as more input types are supported.
Example:
```
def foo(x: TensorLike):
pass
```
This definition passes static type verification for:
```
foo(tf.constant([1, 2, 3]))
foo([1, 2, 3])
foo(np.array([1, 2, 3]))
```
"""),
)
tf_export("types.experimental.TensorLike").export_constant(
__name__, "TensorLike")
np.uint8: (0, 255),
np.uint16: (0, 65535),
np.int8: (-128, 127),
np.int16: (-32768, 32767),
np.int64: (-2**63, 2**63 - 1),
np.uint64: (0, 2**64 - 1),
np.int32: (-2**31, 2**31 - 1),
np.uint32: (0, 2**32 - 1),
np.float32: (-1, 1),
np.float64: (-1, 1)
}
# Define standard wrappers for the types_pb2.DataType enum.
resource = DType(types_pb2.DT_RESOURCE)
doc_typealias.document(
obj=resource,
doc="Handle to a mutable, dynamically allocated resource.")
tf_export("dtypes.resource", "resource").export_constant(__name__, "resource")
variant = DType(types_pb2.DT_VARIANT)
doc_typealias.document(
obj=variant,
doc="Data of arbitrary type (known at runtime).")
tf_export("dtypes.variant", "variant").export_constant(__name__, "variant")
uint8 = DType(types_pb2.DT_UINT8)
doc_typealias.document(
obj=uint8,
doc="Unsigned 8-bit (byte) integer.")
tf_export("dtypes.uint8", "uint8").export_constant(__name__, "uint8")