tf.experimental.dispatch_for_binary_elementwise_assert_apis
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Decorator to override default implementation for binary elementwise assert APIs.
tf.experimental.dispatch_for_binary_elementwise_assert_apis(
x_type, y_type
)
The decorated function (known as the "elementwise assert handler")
overrides the default implementation for any binary elementwise assert API
whenever the value for the first two arguments (typically named x and y)
match the specified type annotations. The handler is called with two
arguments:
elementwise_assert_handler(assert_func, x, y)
Where x and y are the first two arguments to the binary elementwise assert
operation, and assert_func is a TensorFlow function that takes two
parameters and performs the elementwise assert operation (e.g.,
tf.debugging.assert_equal).
The following example shows how this decorator can be used to update all
binary elementwise assert operations to handle a MaskedTensor type:
class MaskedTensor(tf.experimental.ExtensionType):
values: tf.Tensor
mask: tf.Tensor
@dispatch_for_binary_elementwise_assert_apis(MaskedTensor, MaskedTensor)
def binary_elementwise_assert_api_handler(assert_func, x, y):
merged_mask = tf.logical_and(x.mask, y.mask)
selected_x_values = tf.boolean_mask(x.values, merged_mask)
selected_y_values = tf.boolean_mask(y.values, merged_mask)
assert_func(selected_x_values, selected_y_values)
a = MaskedTensor([1, 1, 0, 1, 1], [False, False, True, True, True])
b = MaskedTensor([2, 2, 0, 2, 2], [True, True, True, False, False])
tf.debugging.assert_equal(a, b) # assert passed; no exception was thrown
a = MaskedTensor([1, 1, 1, 1, 1], [True, True, True, True, True])
b = MaskedTensor([0, 0, 0, 0, 2], [True, True, True, True, True])
tf.debugging.assert_greater(a, b)
Traceback (most recent call last):
InvalidArgumentError: Condition x > y did not hold.
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.
|
Registered APIs
The binary elementwise assert APIs are:
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Last updated 2024-04-26 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-04-26 UTC."],[],[],null,["# tf.experimental.dispatch_for_binary_elementwise_assert_apis\n\n\u003cbr /\u003e\n\n|------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.16.1/tensorflow/python/util/dispatch.py#L936-L1002) |\n\nDecorator to override default implementation for binary elementwise assert APIs.\n\n#### View aliases\n\n\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.experimental.dispatch_for_binary_elementwise_assert_apis`](https://www.tensorflow.org/api_docs/python/tf/experimental/dispatch_for_binary_elementwise_assert_apis)\n\n\u003cbr /\u003e\n\n tf.experimental.dispatch_for_binary_elementwise_assert_apis(\n x_type, y_type\n )\n\nThe decorated function (known as the \"elementwise assert handler\")\noverrides the default implementation for any binary elementwise assert API\nwhenever the value for the first two arguments (typically named `x` and `y`)\nmatch the specified type annotations. The handler is called with two\narguments:\n\n`elementwise_assert_handler(assert_func, x, y)`\n\nWhere `x` and `y` are the first two arguments to the binary elementwise assert\noperation, and `assert_func` is a TensorFlow function that takes two\nparameters and performs the elementwise assert operation (e.g.,\n[`tf.debugging.assert_equal`](../../tf/debugging/assert_equal)).\n\nThe following example shows how this decorator can be used to update all\nbinary elementwise assert operations to handle a `MaskedTensor` type: \n\n class MaskedTensor(tf.experimental.ExtensionType):\n values: tf.Tensor\n mask: tf.Tensor\n @dispatch_for_binary_elementwise_assert_apis(MaskedTensor, MaskedTensor)\n def binary_elementwise_assert_api_handler(assert_func, x, y):\n merged_mask = tf.logical_and(x.mask, y.mask)\n selected_x_values = tf.boolean_mask(x.values, merged_mask)\n selected_y_values = tf.boolean_mask(y.values, merged_mask)\n assert_func(selected_x_values, selected_y_values)\n a = MaskedTensor([1, 1, 0, 1, 1], [False, False, True, True, True])\n b = MaskedTensor([2, 2, 0, 2, 2], [True, True, True, False, False])\n tf.debugging.assert_equal(a, b) # assert passed; no exception was thrown\n\n a = MaskedTensor([1, 1, 1, 1, 1], [True, True, True, True, True])\n b = MaskedTensor([0, 0, 0, 0, 2], [True, True, True, True, True])\n tf.debugging.assert_greater(a, b)\n Traceback (most recent call last):\n\n InvalidArgumentError: Condition x \u003e y did not hold.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|----------|---------------------------------------------------------------------|\n| `x_type` | A type annotation indicating when the api handler should be called. |\n| `y_type` | A type annotation indicating when the api handler should be called. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A decorator. ||\n\n\u003cbr /\u003e\n\n#### Registered APIs\n\nThe binary elementwise assert APIs are:\n\n\\\u003c\\\u003e"]]
View source on GitHub