tf.experimental.dispatch_for_binary_elementwise_apis

Decorator to override default implementation for binary elementwise APIs.

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Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.experimental.dispatch_for_binary_elementwise_apis

tf.experimental.dispatch_for_binary_elementwise_apis(
    x_type, y_type
)

The decorated function (known as the "elementwise api handler") overrides the default implementation for any binary elementwise API whenever the value for the first two arguments (typically named x and y) match the specified type annotations. The elementwise api handler is called with two arguments:

elementwise_api_handler(api_func, x, y)

Where x and y are the first two arguments to the elementwise api, and api_func is a TensorFlow function that takes two parameters and performs the elementwise operation (e.g., tf.add).

The following example shows how this decorator can be used to update all binary elementwise operations to handle a MaskedTensor type:

from tensorflow.python.framework import extension_type
class MaskedTensor(extension_type.ExtensionType):
  values: tf.Tensor
  mask: tf.Tensor
@dispatch_for_binary_elementwise_apis(MaskedTensor, MaskedTensor)
def binary_elementwise_api_handler(api_func, x, y):
  return MaskedTensor(api_func(x.values, y.values), x.mask & y.mask)
a = MaskedTensor([1, 2, 3, 4, 5], [True, True, True, True, False])
b = MaskedTensor([2, 4, 6, 8, 0], [True, True, True, False, True])
c = tf.add(a, b)
print(f"values={c.values.numpy()}, mask={c.mask.numpy()}")
values=[ 3 6 9 12 5], mask=[ True True True False False]

Args
x_type	A type annotation indicating when the api handler should be called.
y_type	A type annotation indicating when the api handler should be called.

Returns
A decorator.

Registered APIs

The binary elementwise APIs are:

• tf.bitwise.bitwise_and(x, y, name=None)
• tf.bitwise.bitwise_or(x, y, name=None)
• tf.bitwise.bitwise_xor(x, y, name=None)
• tf.bitwise.left_shift(x, y, name=None)
• tf.bitwise.right_shift(x, y, name=None)
• tf.dtypes.complex(real, imag, name=None)
• tf.math.add(x, y, name=None)
• tf.math.atan2(y, x, name=None)
• tf.math.divide(x, y, name=None)
• tf.math.divide_no_nan(x, y, name=None)
• tf.math.equal(x, y, name=None)
• tf.math.floordiv(x, y, name=None)
• tf.math.floormod(x, y, name=None)
• tf.math.greater(x, y, name=None)
• tf.math.greater_equal(x, y, name=None)
• tf.math.less(x, y, name=None)
• tf.math.less_equal(x, y, name=None)
• tf.math.logical_and(x, y, name=None)
• tf.math.logical_or(x, y, name=None)
• tf.math.logical_xor(x, y, name='LogicalXor')
• tf.math.maximum(x, y, name=None)
• tf.math.minimum(x, y, name=None)
• tf.math.multiply(x, y, name=None)
• tf.math.multiply_no_nan(x, y, name=None)
• tf.math.not_equal(x, y, name=None)
• tf.math.pow(x, y, name=None)
• tf.math.squared_difference(x, y, name=None)
• tf.math.subtract(x, y, name=None)
• tf.math.truediv(x, y, name=None)
• tf.realdiv(x, y, name=None)
• tf.truncatediv(x, y, name=None)
• tf.truncatemod(x, y, name=None)