# tf.experimental.dtensor.DVariable

|---|
| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/dtensor/python/d_variable.py#L144-L245) |

A replacement for tf.Variable which follows initial value placement.

Inherits From: [`Variable`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/Variable)

    tf.experimental.dtensor.DVariable(
        initial_value, *args, dtype=None, **kwargs
    )

The class also handles restore/save operations in DTensor. Note that,
DVariable may fall back to normal tf.Variable at this moment if
`initial_value` is not a DTensor.

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| ## Attributes ||
|---|---|
| `aggregation` | <br /> <br /> |
| `constraint` | Returns the constraint function associated with this variable. |
| `create` | The op responsible for initializing this variable. |
| `device` | The device this variable is on. |
| `dtype` | The dtype of this variable. |
| `graph` | The `Graph` of this variable. |
| `handle` | The handle by which this variable can be accessed. |
| `initial_value` | Returns the Tensor used as the initial value for the variable. |
| `initializer` | The op responsible for initializing this variable. |
| `name` | The name of the handle for this variable. |
| `op` | The op for this variable. |
| `save_as_bf16` | <br /> <br /> |
| `shape` | The shape of this variable. |
| `synchronization` | <br /> <br /> |
| `trainable` | <br /> <br /> |

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## Child Classes

[`class SaveSliceInfo`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/Variable/SaveSliceInfo)

## Methods

### `assign`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/resource_variable_ops.py#L997-L1036)

    assign(
        value, use_locking=None, name=None, read_value=True
    )

Assigns a new value to this variable.

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| Args ||
|---|---|
| `value` | A `Tensor`. The new value for this variable. |
| `use_locking` | If `True`, use locking during the assignment. |
| `name` | The name to use for the assignment. |
| `read_value` | A `bool`. Whether to read and return the new value of the variable or not. |

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| Returns ||
|---|---|
| If `read_value` is `True`, this method will return the new value of the variable after the assignment has completed. Otherwise, when in graph mode it will return the `Operation` that does the assignment, and when in eager mode it will return `None`. ||

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### `assign_add`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/resource_variable_ops.py#L962-L985)

    assign_add(
        delta, use_locking=None, name=None, read_value=True
    )

Adds a value to this variable.

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| Args ||
|---|---|
| `delta` | A `Tensor`. The value to add to this variable. |
| `use_locking` | If `True`, use locking during the operation. |
| `name` | The name to use for the operation. |
| `read_value` | A `bool`. Whether to read and return the new value of the variable or not. |

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| Returns ||
|---|---|
| If `read_value` is `True`, this method will return the new value of the variable after the assignment has completed. Otherwise, when in graph mode it will return the `Operation` that does the assignment, and when in eager mode it will return `None`. ||

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### `assign_sub`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/resource_variable_ops.py#L934-L960)

    assign_sub(
        delta, use_locking=None, name=None, read_value=True
    )

Subtracts a value from this variable.

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| Args ||
|---|---|
| `delta` | A `Tensor`. The value to subtract from this variable. |
| `use_locking` | If `True`, use locking during the operation. |
| `name` | The name to use for the operation. |
| `read_value` | A `bool`. Whether to read and return the new value of the variable or not. |

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| Returns ||
|---|---|
| If `read_value` is `True`, this method will return the new value of the variable after the assignment has completed. Otherwise, when in graph mode it will return the `Operation` that does the assignment, and when in eager mode it will return `None`. ||

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### `batch_scatter_update`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/resource_variable_ops.py#L1219-L1272)

    batch_scatter_update(
        sparse_delta, use_locking=False, name=None
    )

Assigns [`tf.IndexedSlices`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/IndexedSlices) to this variable batch-wise.

Analogous to `batch_gather`. This assumes that this variable and the
sparse_delta IndexedSlices have a series of leading dimensions that are the
same for all of them, and the updates are performed on the last dimension of
indices. In other words, the dimensions should be the following:

`num_prefix_dims = sparse_delta.indices.ndims - 1`
`batch_dim = num_prefix_dims + 1`
`sparse_delta.updates.shape = sparse_delta.indices.shape + var.shape[
batch_dim:]`

where

`sparse_delta.updates.shape[:num_prefix_dims]`
`== sparse_delta.indices.shape[:num_prefix_dims]`
`== var.shape[:num_prefix_dims]`

And the operation performed can be expressed as:

`var[i_1, ..., i_n,
sparse_delta.indices[i_1, ..., i_n, j]] = sparse_delta.updates[
i_1, ..., i_n, j]`

When sparse_delta.indices is a 1D tensor, this operation is equivalent to
`scatter_update`.

To avoid this operation one can looping over the first `ndims` of the
variable and using `scatter_update` on the subtensors that result of slicing
the first dimension. This is a valid option for `ndims = 1`, but less
efficient than this implementation.

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| Args ||
|---|---|
| `sparse_delta` | [`tf.IndexedSlices`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/IndexedSlices) to be assigned to this variable. |
| `use_locking` | If `True`, use locking during the operation. |
| `name` | the name of the operation. |

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| Returns ||
|---|---|
| The updated variable. ||

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| Raises ||
|---|---|
| `TypeError` | if `sparse_delta` is not an `IndexedSlices`. |

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### `count_up_to`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/resource_variable_ops.py#L692-L714)

    count_up_to(
        limit
    )

Increments this variable until it reaches `limit`. (deprecated)

> [!WARNING]
> **Deprecated:** THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Prefer Dataset.range instead.

When that Op is run it tries to increment the variable by `1`. If
incrementing the variable would bring it above `limit` then the Op raises
the exception `OutOfRangeError`.

If no error is raised, the Op outputs the value of the variable before
the increment.

This is essentially a shortcut for `count_up_to(self, limit)`.

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| Args ||
|---|---|
| `limit` | value at which incrementing the variable raises an error. |

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| Returns ||
|---|---|
| A `Tensor` that will hold the variable value before the increment. If no other Op modifies this variable, the values produced will all be distinct. ||

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### `eval`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/resource_variable_ops.py#L679-L684)

    eval(
        session=None
    )

Evaluates and returns the value of this variable.

### `experimental_ref`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/variables.py#L1142-L1144)

    experimental_ref()

DEPRECATED FUNCTION

> [!WARNING]
> **Deprecated:** THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use ref() instead.

### `from_proto`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/resource_variable_ops.py#L911-L917)

    @staticmethod
    from_proto(
        variable_def, import_scope=None
    )

Returns a `Variable` object created from `variable_def`.

### `gather_nd`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/resource_variable_ops.py#L855-L863)

    gather_nd(
        indices, name=None
    )

Reads the value of this variable sparsely, using `gather_nd`.

### `get_shape`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/variables.py#L1106-L1108)

    get_shape()

Alias of [`Variable.shape`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/Variable#shape).

### `initialized_value`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/variables.py#L468-L490)

    initialized_value()

Returns the value of the initialized variable. (deprecated)

> [!WARNING]
> **Deprecated:** THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.

You should use this instead of the variable itself to initialize another
variable with a value that depends on the value of this variable.

    # Initialize 'v' with a random tensor.
    v = tf.Variable(tf.random.truncated_normal([10, 40]))
    # Use `initialized_value` to guarantee that `v` has been
    # initialized before its value is used to initialize `w`.
    # The random values are picked only once.
    w = tf.Variable(v.initialized_value() * 2.0)

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| Returns ||
|---|---|
| A `Tensor` holding the value of this variable after its initializer has run. ||

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### `is_initialized`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/resource_variable_ops.py#L921-L932)

    is_initialized(
        name=None
    )

Checks whether a resource variable has been initialized.

Outputs boolean scalar indicating whether the tensor has been initialized.

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| Args ||
|---|---|
| `name` | A name for the operation (optional). |

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| Returns ||
|---|---|
| A `Tensor` of type `bool`. ||

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### `load`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/variables.py#L922-L965)

    load(
        value, session=None
    )

Load new value into this variable. (deprecated)

> [!WARNING]
> **Deprecated:** THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Prefer Variable.assign which has equivalent behavior in 2.X.

Writes new value to variable's memory. Doesn't add ops to the graph.

This convenience method requires a session where the graph
containing this variable has been launched. If no session is
passed, the default session is used. See [`tf.compat.v1.Session`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/compat/v1/Session) for more
information on launching a graph and on sessions.

    v = tf.Variable([1, 2])
    init = tf.compat.v1.global_variables_initializer()

    with tf.compat.v1.Session() as sess:
        sess.run(init)
        # Usage passing the session explicitly.
        v.load([2, 3], sess)
        print(v.eval(sess)) # prints [2 3]
        # Usage with the default session.  The 'with' block
        # above makes 'sess' the default session.
        v.load([3, 4], sess)
        print(v.eval()) # prints [3 4]

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| Args ||
|---|---|
| `value` | New variable value |
| `session` | The session to use to evaluate this variable. If none, the default session is used. |

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| Raises ||
|---|---|
| `ValueError` | Session is not passed and no default session |

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### `numpy`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/resource_variable_ops.py#L686-L690)

    numpy()

### `read_value`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/resource_variable_ops.py#L805-L818)

    read_value()

Constructs an op which reads the value of this variable.

Should be used when there are multiple reads, or when it is desirable to
read the value only after some condition is true.

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| Returns ||
|---|---|
| The value of the variable. ||

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### `read_value_no_copy`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/resource_variable_ops.py#L820-L834)

    read_value_no_copy()

Constructs an op which reads the value of this variable without copy.

The variable is read without making a copy even when it has been sparsely
accessed. Variables in copy-on-read mode will be converted to copy-on-write
mode.

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| Returns ||
|---|---|
| The value of the variable. ||

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### `ref`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/variables.py#L1146-L1185)

    ref()

Returns a hashable reference object to this Variable.

The primary use case for this API is to put variables in a set/dictionary.
We can't put variables in a set/dictionary as `variable.__hash__()` is no
longer available starting Tensorflow 2.0.

The following will raise an exception starting 2.0

    x = tf.Variable(5)
    y = tf.Variable(10)
    z = tf.Variable(10)
    variable_set = {x, y, z}
    Traceback (most recent call last):

    TypeError: Variable is unhashable. Instead, use tensor.ref() as the key.
    variable_dict = {x: 'five', y: 'ten'}
    Traceback (most recent call last):

    TypeError: Variable is unhashable. Instead, use tensor.ref() as the key.

Instead, we can use `variable.ref()`.

    variable_set = {x.ref(), y.ref(), z.ref()}
    x.ref() in variable_set
    True
    variable_dict = {x.ref(): 'five', y.ref(): 'ten', z.ref(): 'ten'}
    variable_dict[y.ref()]
    'ten'

Also, the reference object provides `.deref()` function that returns the
original Variable.

    x = tf.Variable(5)
    x.ref().deref()
    <tf.Variable 'Variable:0' shape=() dtype=int32, numpy=5>

### `scatter_add`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/resource_variable_ops.py#L1073-L1095)

    scatter_add(
        sparse_delta, use_locking=False, name=None
    )

Adds [`tf.IndexedSlices`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/IndexedSlices) to this variable.

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| Args ||
|---|---|
| `sparse_delta` | [`tf.IndexedSlices`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/IndexedSlices) to be added to this variable. |
| `use_locking` | If `True`, use locking during the operation. |
| `name` | the name of the operation. |

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| Returns ||
|---|---|
| The updated variable. ||

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| Raises ||
|---|---|
| `TypeError` | if `sparse_delta` is not an `IndexedSlices`. |

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### `scatter_div`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/resource_variable_ops.py#L1171-L1193)

    scatter_div(
        sparse_delta, use_locking=False, name=None
    )

Divide this variable by [`tf.IndexedSlices`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/IndexedSlices).

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| Args ||
|---|---|
| `sparse_delta` | [`tf.IndexedSlices`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/IndexedSlices) to divide this variable by. |
| `use_locking` | If `True`, use locking during the operation. |
| `name` | the name of the operation. |

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| Returns ||
|---|---|
| The updated variable. ||

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| Raises ||
|---|---|
| `TypeError` | if `sparse_delta` is not an `IndexedSlices`. |

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### `scatter_max`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/resource_variable_ops.py#L1097-L1120)

    scatter_max(
        sparse_delta, use_locking=False, name=None
    )

Updates this variable with the max of [`tf.IndexedSlices`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/IndexedSlices) and itself.

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| Args ||
|---|---|
| `sparse_delta` | [`tf.IndexedSlices`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/IndexedSlices) to use as an argument of max with this variable. |
| `use_locking` | If `True`, use locking during the operation. |
| `name` | the name of the operation. |

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| Returns ||
|---|---|
| The updated variable. ||

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| Raises ||
|---|---|
| `TypeError` | if `sparse_delta` is not an `IndexedSlices`. |

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### `scatter_min`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/resource_variable_ops.py#L1122-L1145)

    scatter_min(
        sparse_delta, use_locking=False, name=None
    )

Updates this variable with the min of [`tf.IndexedSlices`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/IndexedSlices) and itself.

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| Args ||
|---|---|
| `sparse_delta` | [`tf.IndexedSlices`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/IndexedSlices) to use as an argument of min with this variable. |
| `use_locking` | If `True`, use locking during the operation. |
| `name` | the name of the operation. |

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| Returns ||
|---|---|
| The updated variable. ||

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| Raises ||
|---|---|
| `TypeError` | if `sparse_delta` is not an `IndexedSlices`. |

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### `scatter_mul`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/resource_variable_ops.py#L1147-L1169)

    scatter_mul(
        sparse_delta, use_locking=False, name=None
    )

Multiply this variable by [`tf.IndexedSlices`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/IndexedSlices).

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| Args ||
|---|---|
| `sparse_delta` | [`tf.IndexedSlices`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/IndexedSlices) to multiply this variable by. |
| `use_locking` | If `True`, use locking during the operation. |
| `name` | the name of the operation. |

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| Returns ||
|---|---|
| The updated variable. ||

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| Raises ||
|---|---|
| `TypeError` | if `sparse_delta` is not an `IndexedSlices`. |

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### `scatter_nd_add`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/resource_variable_ops.py#L1326-L1376)

    scatter_nd_add(
        indices, updates, name=None
    )

Applies sparse addition to individual values or slices in a Variable.

`ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.

`indices` must be integer tensor, containing indices into `ref`.
It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.

The innermost dimension of `indices` (with length `K`) corresponds to
indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th
dimension of `ref`.

`updates` is `Tensor` of rank `Q-1+P-K` with shape:

    [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].

For example, say we want to add 4 scattered elements to a rank-1 tensor to
8 elements. In Python, that update would look like this:

        ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
        indices = tf.constant([[4], [3], [1] ,[7]])
        updates = tf.constant([9, 10, 11, 12])
        add = ref.scatter_nd_add(indices, updates)
        with tf.compat.v1.Session() as sess:
          print sess.run(add)

The resulting update to ref would look like this:

    [1, 13, 3, 14, 14, 6, 7, 20]

See [`tf.scatter_nd`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/scatter_nd) for more details about how to make updates to
slices.

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| Args ||
|---|---|
| `indices` | The indices to be used in the operation. |
| `updates` | The values to be used in the operation. |
| `name` | the name of the operation. |

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| Returns ||
|---|---|
| The updated variable. ||

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### `scatter_nd_max`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/resource_variable_ops.py#L1430-L1464)

    scatter_nd_max(
        indices, updates, name=None
    )

Updates this variable with the max of [`tf.IndexedSlices`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/IndexedSlices) and itself.

`ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.

`indices` must be integer tensor, containing indices into `ref`.
It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.

The innermost dimension of `indices` (with length `K`) corresponds to
indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th
dimension of `ref`.

`updates` is `Tensor` of rank `Q-1+P-K` with shape:

    [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].

See [`tf.scatter_nd`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/scatter_nd) for more details about how to make updates to
slices.

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| Args ||
|---|---|
| `indices` | The indices to be used in the operation. |
| `updates` | The values to be used in the operation. |
| `name` | the name of the operation. |

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| Returns ||
|---|---|
| The updated variable. ||

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### `scatter_nd_min`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/resource_variable_ops.py#L1466-L1500)

    scatter_nd_min(
        indices, updates, name=None
    )

Updates this variable with the min of [`tf.IndexedSlices`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/IndexedSlices) and itself.

`ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.

`indices` must be integer tensor, containing indices into `ref`.
It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.

The innermost dimension of `indices` (with length `K`) corresponds to
indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th
dimension of `ref`.

`updates` is `Tensor` of rank `Q-1+P-K` with shape:

    [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].

See [`tf.scatter_nd`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/scatter_nd) for more details about how to make updates to
slices.

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| Args ||
|---|---|
| `indices` | The indices to be used in the operation. |
| `updates` | The values to be used in the operation. |
| `name` | the name of the operation. |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| The updated variable. ||

<br />

### `scatter_nd_sub`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/resource_variable_ops.py#L1274-L1324)

    scatter_nd_sub(
        indices, updates, name=None
    )

Applies sparse subtraction to individual values or slices in a Variable.

`ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.

`indices` must be integer tensor, containing indices into `ref`.
It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.

The innermost dimension of `indices` (with length `K`) corresponds to
indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th
dimension of `ref`.

`updates` is `Tensor` of rank `Q-1+P-K` with shape:

    [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].

For example, say we want to add 4 scattered elements to a rank-1 tensor to
8 elements. In Python, that update would look like this:

        ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
        indices = tf.constant([[4], [3], [1] ,[7]])
        updates = tf.constant([9, 10, 11, 12])
        op = ref.scatter_nd_sub(indices, updates)
        with tf.compat.v1.Session() as sess:
          print sess.run(op)

The resulting update to ref would look like this:

    [1, -9, 3, -6, -6, 6, 7, -4]

See [`tf.scatter_nd`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/scatter_nd) for more details about how to make updates to
slices.

<br />

<br />

<br />

| Args ||
|---|---|
| `indices` | The indices to be used in the operation. |
| `updates` | The values to be used in the operation. |
| `name` | the name of the operation. |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| The updated variable. ||

<br />

### `scatter_nd_update`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/resource_variable_ops.py#L1378-L1428)

    scatter_nd_update(
        indices, updates, name=None
    )

Applies sparse assignment to individual values or slices in a Variable.

`ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.

`indices` must be integer tensor, containing indices into `ref`.
It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.

The innermost dimension of `indices` (with length `K`) corresponds to
indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th
dimension of `ref`.

`updates` is `Tensor` of rank `Q-1+P-K` with shape:

    [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].

For example, say we want to add 4 scattered elements to a rank-1 tensor to
8 elements. In Python, that update would look like this:

        ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
        indices = tf.constant([[4], [3], [1] ,[7]])
        updates = tf.constant([9, 10, 11, 12])
        op = ref.scatter_nd_update(indices, updates)
        with tf.compat.v1.Session() as sess:
          print sess.run(op)

The resulting update to ref would look like this:

    [1, 11, 3, 10, 9, 6, 7, 12]

See [`tf.scatter_nd`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/scatter_nd) for more details about how to make updates to
slices.

<br />

<br />

<br />

| Args ||
|---|---|
| `indices` | The indices to be used in the operation. |
| `updates` | The values to be used in the operation. |
| `name` | the name of the operation. |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| The updated variable. ||

<br />

### `scatter_sub`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/resource_variable_ops.py#L1049-L1071)

    scatter_sub(
        sparse_delta, use_locking=False, name=None
    )

Subtracts [`tf.IndexedSlices`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/IndexedSlices) from this variable.

<br />

<br />

<br />

| Args ||
|---|---|
| `sparse_delta` | [`tf.IndexedSlices`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/IndexedSlices) to be subtracted from this variable. |
| `use_locking` | If `True`, use locking during the operation. |
| `name` | the name of the operation. |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| The updated variable. ||

<br />

<br />

<br />

<br />

| Raises ||
|---|---|
| `TypeError` | if `sparse_delta` is not an `IndexedSlices`. |

<br />

### `scatter_update`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/resource_variable_ops.py#L1195-L1217)

    scatter_update(
        sparse_delta, use_locking=False, name=None
    )

Assigns [`tf.IndexedSlices`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/IndexedSlices) to this variable.

<br />

<br />

<br />

| Args ||
|---|---|
| `sparse_delta` | [`tf.IndexedSlices`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/IndexedSlices) to be assigned to this variable. |
| `use_locking` | If `True`, use locking during the operation. |
| `name` | the name of the operation. |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| The updated variable. ||

<br />

<br />

<br />

<br />

| Raises ||
|---|---|
| `TypeError` | if `sparse_delta` is not an `IndexedSlices`. |

<br />

### `set_shape`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/resource_variable_ops.py#L601-L602)

    set_shape(
        shape
    )

Overrides the shape for this variable.

<br />

<br />

<br />

| Args ||
|---|---|
| `shape` | the `TensorShape` representing the overridden shape. |

<br />

### `sparse_read`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/resource_variable_ops.py#L836-L853)

    sparse_read(
        indices, name=None
    )

Reads the value of this variable sparsely, using `gather`.

### `to_proto`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/resource_variable_ops.py#L865-L909)

    to_proto(
        export_scope=None
    )

Converts a `ResourceVariable` to a `VariableDef` protocol buffer.

<br />

<br />

<br />

| Args ||
|---|---|
| `export_scope` | Optional `string`. Name scope to remove. |

<br />

<br />

<br />

<br />

| Raises ||
|---|---|
| `RuntimeError` | If run in EAGER mode. |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| A `VariableDef` protocol buffer, or `None` if the `Variable` is not in the specified name scope. ||

<br />

### `value`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/resource_variable_ops.py#L628-L633)

    value()

A cached operation which reads the value of this variable.

### `__abs__`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/math_ops.py#L365-L409)

    __abs__(
        name=None
    )

Computes the absolute value of a tensor.

Given a tensor of integer or floating-point values, this operation returns a
tensor of the same type, where each element contains the absolute value of the
corresponding element in the input.

Given a tensor `x` of complex numbers, this operation returns a tensor of type
`float32` or `float64` that is the absolute value of each element in `x`. For
a complex number \\(a + bj\\), its absolute value is computed as
\\(\\sqrt{a\^2 + b\^2}\\).

#### For example:

    # real number
    x = tf.constant([-2.25, 3.25])
    tf.abs(x)
    <tf.Tensor: shape=(2,), dtype=float32,
    numpy=array([2.25, 3.25], dtype=float32)>

    # complex number
    x = tf.constant([[-2.25 + 4.75j], [-3.25 + 5.75j]])
    tf.abs(x)
    <tf.Tensor: shape=(2, 1), dtype=float64, numpy=
    array([[5.25594901],
           [6.60492241]])>

<br />

<br />

<br />

| Args ||
|---|---|
| `x` | A `Tensor` or `SparseTensor` of type `float16`, `float32`, `float64`, `int32`, `int64`, `complex64` or `complex128`. |
| `name` | A name for the operation (optional). |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| A `Tensor` or `SparseTensor` of the same size, type and sparsity as `x`, with absolute values. Note, for `complex64` or `complex128` input, the returned `Tensor` will be of type `float32` or `float64`, respectively. ||

<br />

### `__add__`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/math_ops.py#L1458-L1484)

    __add__(
        y
    )

The operation invoked by the [`Tensor.add`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/Tensor#__add__) operator.

<br />

<br />

<br />

| Purpose in the API ||
|---|---|
| This method is exposed in TensorFlow's API so that library developers can register dispatching for [`Tensor.add`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/Tensor#__add__) to allow it to handle custom composite tensors \& other custom objects. <br /> The API symbol is not intended to be called by users directly and does appear in TensorFlow's generated documentation. ||

<br />

<br />

<br />

<br />

| Args ||
|---|---|
| `x` | The left-hand side of the `+` operator. |
| `y` | The right-hand side of the `+` operator. |
| `name` | an optional name for the operation. |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| The result of the elementwise `+` operation. ||

<br />

### `__and__`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/math_ops.py#L1458-L1484)

    __and__(
        y
    )

### `__array__`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/resource_variable_ops.py#L534-L549)

    __array__(
        dtype=None
    )

Allows direct conversion to a numpy array.

    np.array(tf.Variable([1.0]))
    array([1.], dtype=float32)

<br />

<br />

<br />

| Returns ||
|---|---|
| The variable value as a numpy array. ||

<br />

### `__bool__`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/resource_variable_ops.py#L554-L555)

    __bool__()

### `__div__`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/math_ops.py#L1458-L1484)

    __div__(
        y
    )

Divides x / y elementwise (using Python 2 division operator semantics). (deprecated)

> [!WARNING]
> **Deprecated:** THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Deprecated in favor of operator or tf.math.divide.

This function divides `x` and `y`, forcing Python 2 semantics. That is, if `x`
and `y` are both integers then the result will be an integer. This is in
contrast to Python 3, where division with `/` is always a float while division
with `//` is always an integer.

<br />

<br />

<br />

| Args ||
|---|---|
| `x` | `Tensor` numerator of real numeric type. |
| `y` | `Tensor` denominator of real numeric type. |
| `name` | A name for the operation (optional). |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| `x / y` returns the quotient of x and y. ||

<br />

<br />

#### Migrate to TF2

<br />

This function is deprecated in TF2. Prefer using the Tensor division operator,
[`tf.divide`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/math/divide), or [`tf.math.divide`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/math/divide), which obey the Python 3 division operator
semantics.

<br />

<br />

### `__eq__`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/variables.py#L1025-L1031)

    __eq__(
        other
    )

Compares two variables element-wise for equality.

### `__floordiv__`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/math_ops.py#L1458-L1484)

    __floordiv__(
        y
    )

Divides `x / y` elementwise, rounding toward the most negative integer.

Mathematically, this is equivalent to floor(x / y). For example:
floor(8.4 / 4.0) = floor(2.1) = 2.0
floor(-8.4 / 4.0) = floor(-2.1) = -3.0
This is equivalent to the '//' operator in Python 3.0 and above.

> [!NOTE]
> **Note:** `x` and `y` must have the same type, and the result will have the same type as well.

<br />

<br />

<br />

| Args ||
|---|---|
| `x` | `Tensor` numerator of real numeric type. |
| `y` | `Tensor` denominator of real numeric type. |
| `name` | A name for the operation (optional). |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| `x / y` rounded toward -infinity. ||

<br />

<br />

<br />

<br />

| Raises ||
|---|---|
| `TypeError` | If the inputs are complex. |

<br />

### `__ge__`

    __ge__(
        y, name=None
    )

Returns the truth value of (x \>= y) element-wise.

> [!NOTE]
> **Note:** [`math.greater_equal`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/math/greater_equal) supports broadcasting. More about broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)

#### Example:

    x = tf.constant([5, 4, 6, 7])
    y = tf.constant([5, 2, 5, 10])
    tf.math.greater_equal(x, y) ==> [True, True, True, False]

    x = tf.constant([5, 4, 6, 7])
    y = tf.constant([5])
    tf.math.greater_equal(x, y) ==> [True, False, True, True]

<br />

<br />

<br />

| Args ||
|---|---|
| `x` | A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`. |
| `y` | A `Tensor`. Must have the same type as `x`. |
| `name` | A name for the operation (optional). |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| A `Tensor` of type `bool`. ||

<br />

### `__getitem__`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/array_ops.py#L1329-L1370)

    __getitem__(
        slice_spec
    )

Creates a slice helper object given a variable.

This allows creating a sub-tensor from part of the current contents
of a variable. See [`tf.Tensor.getitem`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/Tensor#__getitem__) for detailed examples
of slicing.

This function in addition also allows assignment to a sliced range.
This is similar to `__setitem__` functionality in Python. However,
the syntax is different so that the user can capture the assignment
operation for grouping or passing to `sess.run()` in TF1.
For example,

    import tensorflow as tf
    A = tf.Variable([[1,2,3], [4,5,6], [7,8,9]], dtype=tf.float32)
    print(A[:2, :2])  # => [[1,2], [4,5]]

    A[:2,:2].assign(22. * tf.ones((2, 2))))
    print(A) # => [[22, 22, 3], [22, 22, 6], [7,8,9]]

Note that assignments currently do not support NumPy broadcasting
semantics.

<br />

<br />

<br />

| Args ||
|---|---|
| `var` | An `ops.Variable` object. |
| `slice_spec` | The arguments to [`Tensor.getitem`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/Tensor#__getitem__). |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| The appropriate slice of "tensor", based on "slice_spec". As an operator. The operator also has a `assign()` method that can be used to generate an assignment operator. ||

<br />

<br />

<br />

<br />

| Raises ||
|---|---|
| `ValueError` | If a slice range is negative size. |
| `TypeError` | TypeError: If the slice indices aren't int, slice, ellipsis, tf.newaxis or int32/int64 tensors. |

<br />

### `__gt__`

    __gt__(
        y, name=None
    )

Returns the truth value of (x \> y) element-wise.

> [!NOTE]
> **Note:** [`math.greater`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/math/greater) supports broadcasting. More about broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)

#### Example:

    x = tf.constant([5, 4, 6])
    y = tf.constant([5, 2, 5])
    tf.math.greater(x, y) ==> [False, True, True]

    x = tf.constant([5, 4, 6])
    y = tf.constant([5])
    tf.math.greater(x, y) ==> [False, False, True]

<br />

<br />

<br />

| Args ||
|---|---|
| `x` | A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`. |
| `y` | A `Tensor`. Must have the same type as `x`. |
| `name` | A name for the operation (optional). |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| A `Tensor` of type `bool`. ||

<br />

### `__invert__`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/math_ops.py#L1940-L1943)

    __invert__(
        name=None
    )

### `__iter__`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/variables.py#L1042-L1044)

    __iter__()

When executing eagerly, iterates over the value of the variable.

### `__le__`

    __le__(
        y, name=None
    )

Returns the truth value of (x \<= y) element-wise.

> [!NOTE]
> **Note:** [`math.less_equal`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/math/less_equal) supports broadcasting. More about broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)

#### Example:

    x = tf.constant([5, 4, 6])
    y = tf.constant([5])
    tf.math.less_equal(x, y) ==> [True, True, False]

    x = tf.constant([5, 4, 6])
    y = tf.constant([5, 6, 6])
    tf.math.less_equal(x, y) ==> [True, True, True]

<br />

<br />

<br />

| Args ||
|---|---|
| `x` | A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`. |
| `y` | A `Tensor`. Must have the same type as `x`. |
| `name` | A name for the operation (optional). |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| A `Tensor` of type `bool`. ||

<br />

### `__lt__`

    __lt__(
        y, name=None
    )

Returns the truth value of (x \< y) element-wise.

> [!NOTE]
> **Note:** [`math.less`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/math/less) supports broadcasting. More about broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)

#### Example:

    x = tf.constant([5, 4, 6])
    y = tf.constant([5])
    tf.math.less(x, y) ==> [False, True, False]

    x = tf.constant([5, 4, 6])
    y = tf.constant([5, 6, 7])
    tf.math.less(x, y) ==> [False, True, True]

<br />

<br />

<br />

| Args ||
|---|---|
| `x` | A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`. |
| `y` | A `Tensor`. Must have the same type as `x`. |
| `name` | A name for the operation (optional). |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| A `Tensor` of type `bool`. ||

<br />

### `__matmul__`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/math_ops.py#L1458-L1484)

    __matmul__(
        y
    )

Multiplies matrix `a` by matrix `b`, producing `a` \* `b`.

The inputs must, following any transpositions, be tensors of rank \>= 2
where the inner 2 dimensions specify valid matrix multiplication dimensions,
and any further outer dimensions specify matching batch size.

Both matrices must be of the same type. The supported types are:
`bfloat16`, `float16`, `float32`, `float64`, `int32`, `int64`,
`complex64`, `complex128`.

Either matrix can be transposed or adjointed (conjugated and transposed) on
the fly by setting one of the corresponding flag to `True`. These are `False`
by default.

If one or both of the matrices contain a lot of zeros, a more efficient
multiplication algorithm can be used by setting the corresponding
`a_is_sparse` or `b_is_sparse` flag to `True`. These are `False` by default.
This optimization is only available for plain matrices (rank-2 tensors) with
datatypes `bfloat16` or `float32`.

A simple 2-D tensor matrix multiplication:

    a = tf.constant([1, 2, 3, 4, 5, 6], shape=[2, 3])
    a  # 2-D tensor
    <tf.Tensor: shape=(2, 3), dtype=int32, numpy=
    array([[1, 2, 3],
           [4, 5, 6]], dtype=int32)>
    b = tf.constant([7, 8, 9, 10, 11, 12], shape=[3, 2])
    b  # 2-D tensor
    <tf.Tensor: shape=(3, 2), dtype=int32, numpy=
    array([[ 7,  8],
           [ 9, 10],
           [11, 12]], dtype=int32)>
    c = tf.matmul(a, b)
    c  # `a` * `b`
    <tf.Tensor: shape=(2, 2), dtype=int32, numpy=
    array([[ 58,  64],
           [139, 154]], dtype=int32)>

A batch matrix multiplication with batch shape \[2\]:

    a = tf.constant(np.arange(1, 13, dtype=np.int32), shape=[2, 2, 3])
    a  # 3-D tensor
    <tf.Tensor: shape=(2, 2, 3), dtype=int32, numpy=
    array([[[ 1,  2,  3],
            [ 4,  5,  6]],
           [[ 7,  8,  9],
            [10, 11, 12]]], dtype=int32)>
    b = tf.constant(np.arange(13, 25, dtype=np.int32), shape=[2, 3, 2])
    b  # 3-D tensor
    <tf.Tensor: shape=(2, 3, 2), dtype=int32, numpy=
    array([[[13, 14],
            [15, 16],
            [17, 18]],
           [[19, 20],
            [21, 22],
            [23, 24]]], dtype=int32)>
    c = tf.matmul(a, b)
    c  # `a` * `b`
    <tf.Tensor: shape=(2, 2, 2), dtype=int32, numpy=
    array([[[ 94, 100],
            [229, 244]],
           [[508, 532],
            [697, 730]]], dtype=int32)>

Since python \>= 3.5 the @ operator is supported
(see [PEP 465](https://www.python.org/dev/peps/pep-0465/)). In TensorFlow,
it simply calls the [`tf.matmul()`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/linalg/matmul) function, so the following lines are
equivalent:

    d = a @ b @ [[10], [11]]
    d = tf.matmul(tf.matmul(a, b), [[10], [11]])

<br />

<br />

<br />

| Args ||
|---|---|
| `a` | [`tf.Tensor`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/Tensor) of type `float16`, `float32`, `float64`, `int32`, `complex64`, `complex128` and rank \> 1. |
| `b` | [`tf.Tensor`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/Tensor) with same type and rank as `a`. |
| `transpose_a` | If `True`, `a` is transposed before multiplication. |
| `transpose_b` | If `True`, `b` is transposed before multiplication. |
| `adjoint_a` | If `True`, `a` is conjugated and transposed before multiplication. |
| `adjoint_b` | If `True`, `b` is conjugated and transposed before multiplication. |
| `a_is_sparse` | If `True`, `a` is treated as a sparse matrix. Notice, this **does not support [`tf.sparse.SparseTensor`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/sparse/SparseTensor)** , it just makes optimizations that assume most values in `a` are zero. See [`tf.sparse.sparse_dense_matmul`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/sparse/sparse_dense_matmul) for some support for [`tf.sparse.SparseTensor`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/sparse/SparseTensor) multiplication. |
| `b_is_sparse` | If `True`, `b` is treated as a sparse matrix. Notice, this **does not support [`tf.sparse.SparseTensor`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/sparse/SparseTensor)** , it just makes optimizations that assume most values in `b` are zero. See [`tf.sparse.sparse_dense_matmul`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/sparse/sparse_dense_matmul) for some support for [`tf.sparse.SparseTensor`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/sparse/SparseTensor) multiplication. |
| `output_type` | The output datatype if needed. Defaults to None in which case the output_type is the same as input type. Currently only works when input tensors are type (u)int8 and output_type can be int32. |
| `name` | Name for the operation (optional). |

<br />

<br />

| Returns ||
|---|---|
| A [`tf.Tensor`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/Tensor) of the same type as `a` and `b` where each inner-most matrix is the product of the corresponding matrices in `a` and `b`, e.g. if all transpose or adjoint attributes are `False`: <br /> `output[..., i, j] = sum_k (a[..., i, k] * b[..., k, j])`, for all indices `i`, `j`. ||
| `Note` | This is matrix product, not element-wise product. |

<br />

<br />

<br />

<br />

| Raises ||
|---|---|
| `ValueError` | If `transpose_a` and `adjoint_a`, or `transpose_b` and `adjoint_b` are both set to `True`. |
| `TypeError` | If output_type is specified but the types of `a`, `b` and `output_type` is not (u)int8, (u)int8 and int32. |

<br />

### `__mod__`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/math_ops.py#L1458-L1484)

    __mod__(
        y
    )

Returns element-wise remainder of division.

This follows Python semantics in that the
result here is consistent with a flooring divide. E.g.
`floor(x / y) * y + floormod(x, y) = x`, regardless of the signs of x and y.

> [!NOTE]
> **Note:** [`math.floormod`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/math/floormod) supports broadcasting. More about broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)

<br />

<br />

<br />

| Args ||
|---|---|
| `x` | A `Tensor`. Must be one of the following types: `int8`, `int16`, `int32`, `int64`, `uint8`, `uint16`, `uint32`, `uint64`, `bfloat16`, `half`, `float32`, `float64`. |
| `y` | A `Tensor`. Must have the same type as `x`. |
| `name` | A name for the operation (optional). |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| A `Tensor`. Has the same type as `x`. ||

<br />

### `__mul__`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/math_ops.py#L1458-L1484)

    __mul__(
        y
    )

Dispatches cwise mul for "Dense*Dense" and "Dense*Sparse".

### `__ne__`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/variables.py#L1034-L1040)

    __ne__(
        other
    )

Compares two variables element-wise for equality.

### `__neg__`

    __neg__(
        name=None
    )

Computes numerical negative value element-wise.

I.e., \\(y = -x\\).

<br />

<br />

<br />

| Args ||
|---|---|
| `x` | A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `int8`, `int16`, `int32`, `int64`, `complex64`, `complex128`. |
| `name` | A name for the operation (optional). |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| A `Tensor`. Has the same type as `x`. ||

<br />

### `__nonzero__`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/resource_variable_ops.py#L551-L552)

    __nonzero__()

### `__or__`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/math_ops.py#L1458-L1484)

    __or__(
        y
    )

### `__pow__`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/math_ops.py#L1458-L1484)

    __pow__(
        y
    )

Computes the power of one value to another.

Given a tensor `x` and a tensor `y`, this operation computes \\(x\^y\\) for
corresponding elements in `x` and `y`. For example:

    x = tf.constant([[2, 2], [3, 3]])
    y = tf.constant([[8, 16], [2, 3]])
    tf.pow(x, y)  # [[256, 65536], [9, 27]]

<br />

<br />

<br />

| Args ||
|---|---|
| `x` | A `Tensor` of type `float16`, `float32`, `float64`, `int32`, `int64`, `complex64`, or `complex128`. |
| `y` | A `Tensor` of type `float16`, `float32`, `float64`, `int32`, `int64`, `complex64`, or `complex128`. |
| `name` | A name for the operation (optional). |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| A `Tensor`. ||

<br />

### `__radd__`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/math_ops.py#L1495-L1501)

    __radd__(
        x
    )

The operation invoked by the [`Tensor.add`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/Tensor#__add__) operator.

<br />

<br />

<br />

| Purpose in the API ||
|---|---|
| This method is exposed in TensorFlow's API so that library developers can register dispatching for [`Tensor.add`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/Tensor#__add__) to allow it to handle custom composite tensors \& other custom objects. <br /> The API symbol is not intended to be called by users directly and does appear in TensorFlow's generated documentation. ||

<br />

<br />

<br />

<br />

| Args ||
|---|---|
| `x` | The left-hand side of the `+` operator. |
| `y` | The right-hand side of the `+` operator. |
| `name` | an optional name for the operation. |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| The result of the elementwise `+` operation. ||

<br />

### `__rand__`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/math_ops.py#L1495-L1501)

    __rand__(
        x
    )

### `__rdiv__`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/math_ops.py#L1495-L1501)

    __rdiv__(
        x
    )

Divides x / y elementwise (using Python 2 division operator semantics). (deprecated)

> [!WARNING]
> **Deprecated:** THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Deprecated in favor of operator or tf.math.divide.

This function divides `x` and `y`, forcing Python 2 semantics. That is, if `x`
and `y` are both integers then the result will be an integer. This is in
contrast to Python 3, where division with `/` is always a float while division
with `//` is always an integer.

<br />

<br />

<br />

| Args ||
|---|---|
| `x` | `Tensor` numerator of real numeric type. |
| `y` | `Tensor` denominator of real numeric type. |
| `name` | A name for the operation (optional). |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| `x / y` returns the quotient of x and y. ||

<br />

<br />

#### Migrate to TF2

<br />

This function is deprecated in TF2. Prefer using the Tensor division operator,
[`tf.divide`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/math/divide), or [`tf.math.divide`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/math/divide), which obey the Python 3 division operator
semantics.

<br />

<br />

### `__rfloordiv__`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/math_ops.py#L1495-L1501)

    __rfloordiv__(
        x
    )

Divides `x / y` elementwise, rounding toward the most negative integer.

Mathematically, this is equivalent to floor(x / y). For example:
floor(8.4 / 4.0) = floor(2.1) = 2.0
floor(-8.4 / 4.0) = floor(-2.1) = -3.0
This is equivalent to the '//' operator in Python 3.0 and above.

> [!NOTE]
> **Note:** `x` and `y` must have the same type, and the result will have the same type as well.

<br />

<br />

<br />

| Args ||
|---|---|
| `x` | `Tensor` numerator of real numeric type. |
| `y` | `Tensor` denominator of real numeric type. |
| `name` | A name for the operation (optional). |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| `x / y` rounded toward -infinity. ||

<br />

<br />

<br />

<br />

| Raises ||
|---|---|
| `TypeError` | If the inputs are complex. |

<br />

### `__rmatmul__`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/math_ops.py#L1495-L1501)

    __rmatmul__(
        x
    )

Multiplies matrix `a` by matrix `b`, producing `a` \* `b`.

The inputs must, following any transpositions, be tensors of rank \>= 2
where the inner 2 dimensions specify valid matrix multiplication dimensions,
and any further outer dimensions specify matching batch size.

Both matrices must be of the same type. The supported types are:
`bfloat16`, `float16`, `float32`, `float64`, `int32`, `int64`,
`complex64`, `complex128`.

Either matrix can be transposed or adjointed (conjugated and transposed) on
the fly by setting one of the corresponding flag to `True`. These are `False`
by default.

If one or both of the matrices contain a lot of zeros, a more efficient
multiplication algorithm can be used by setting the corresponding
`a_is_sparse` or `b_is_sparse` flag to `True`. These are `False` by default.
This optimization is only available for plain matrices (rank-2 tensors) with
datatypes `bfloat16` or `float32`.

A simple 2-D tensor matrix multiplication:

    a = tf.constant([1, 2, 3, 4, 5, 6], shape=[2, 3])
    a  # 2-D tensor
    <tf.Tensor: shape=(2, 3), dtype=int32, numpy=
    array([[1, 2, 3],
           [4, 5, 6]], dtype=int32)>
    b = tf.constant([7, 8, 9, 10, 11, 12], shape=[3, 2])
    b  # 2-D tensor
    <tf.Tensor: shape=(3, 2), dtype=int32, numpy=
    array([[ 7,  8],
           [ 9, 10],
           [11, 12]], dtype=int32)>
    c = tf.matmul(a, b)
    c  # `a` * `b`
    <tf.Tensor: shape=(2, 2), dtype=int32, numpy=
    array([[ 58,  64],
           [139, 154]], dtype=int32)>

A batch matrix multiplication with batch shape \[2\]:

    a = tf.constant(np.arange(1, 13, dtype=np.int32), shape=[2, 2, 3])
    a  # 3-D tensor
    <tf.Tensor: shape=(2, 2, 3), dtype=int32, numpy=
    array([[[ 1,  2,  3],
            [ 4,  5,  6]],
           [[ 7,  8,  9],
            [10, 11, 12]]], dtype=int32)>
    b = tf.constant(np.arange(13, 25, dtype=np.int32), shape=[2, 3, 2])
    b  # 3-D tensor
    <tf.Tensor: shape=(2, 3, 2), dtype=int32, numpy=
    array([[[13, 14],
            [15, 16],
            [17, 18]],
           [[19, 20],
            [21, 22],
            [23, 24]]], dtype=int32)>
    c = tf.matmul(a, b)
    c  # `a` * `b`
    <tf.Tensor: shape=(2, 2, 2), dtype=int32, numpy=
    array([[[ 94, 100],
            [229, 244]],
           [[508, 532],
            [697, 730]]], dtype=int32)>

Since python \>= 3.5 the @ operator is supported
(see [PEP 465](https://www.python.org/dev/peps/pep-0465/)). In TensorFlow,
it simply calls the [`tf.matmul()`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/linalg/matmul) function, so the following lines are
equivalent:

    d = a @ b @ [[10], [11]]
    d = tf.matmul(tf.matmul(a, b), [[10], [11]])

<br />

<br />

<br />

| Args ||
|---|---|
| `a` | [`tf.Tensor`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/Tensor) of type `float16`, `float32`, `float64`, `int32`, `complex64`, `complex128` and rank \> 1. |
| `b` | [`tf.Tensor`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/Tensor) with same type and rank as `a`. |
| `transpose_a` | If `True`, `a` is transposed before multiplication. |
| `transpose_b` | If `True`, `b` is transposed before multiplication. |
| `adjoint_a` | If `True`, `a` is conjugated and transposed before multiplication. |
| `adjoint_b` | If `True`, `b` is conjugated and transposed before multiplication. |
| `a_is_sparse` | If `True`, `a` is treated as a sparse matrix. Notice, this **does not support [`tf.sparse.SparseTensor`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/sparse/SparseTensor)** , it just makes optimizations that assume most values in `a` are zero. See [`tf.sparse.sparse_dense_matmul`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/sparse/sparse_dense_matmul) for some support for [`tf.sparse.SparseTensor`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/sparse/SparseTensor) multiplication. |
| `b_is_sparse` | If `True`, `b` is treated as a sparse matrix. Notice, this **does not support [`tf.sparse.SparseTensor`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/sparse/SparseTensor)** , it just makes optimizations that assume most values in `b` are zero. See [`tf.sparse.sparse_dense_matmul`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/sparse/sparse_dense_matmul) for some support for [`tf.sparse.SparseTensor`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/sparse/SparseTensor) multiplication. |
| `output_type` | The output datatype if needed. Defaults to None in which case the output_type is the same as input type. Currently only works when input tensors are type (u)int8 and output_type can be int32. |
| `name` | Name for the operation (optional). |

<br />

<br />

| Returns ||
|---|---|
| A [`tf.Tensor`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/Tensor) of the same type as `a` and `b` where each inner-most matrix is the product of the corresponding matrices in `a` and `b`, e.g. if all transpose or adjoint attributes are `False`: <br /> `output[..., i, j] = sum_k (a[..., i, k] * b[..., k, j])`, for all indices `i`, `j`. ||
| `Note` | This is matrix product, not element-wise product. |

<br />

<br />

<br />

<br />

| Raises ||
|---|---|
| `ValueError` | If `transpose_a` and `adjoint_a`, or `transpose_b` and `adjoint_b` are both set to `True`. |
| `TypeError` | If output_type is specified but the types of `a`, `b` and `output_type` is not (u)int8, (u)int8 and int32. |

<br />

### `__rmod__`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/math_ops.py#L1495-L1501)

    __rmod__(
        x
    )

Returns element-wise remainder of division.

This follows Python semantics in that the
result here is consistent with a flooring divide. E.g.
`floor(x / y) * y + floormod(x, y) = x`, regardless of the signs of x and y.

> [!NOTE]
> **Note:** [`math.floormod`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/math/floormod) supports broadcasting. More about broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)

<br />

<br />

<br />

| Args ||
|---|---|
| `x` | A `Tensor`. Must be one of the following types: `int8`, `int16`, `int32`, `int64`, `uint8`, `uint16`, `uint32`, `uint64`, `bfloat16`, `half`, `float32`, `float64`. |
| `y` | A `Tensor`. Must have the same type as `x`. |
| `name` | A name for the operation (optional). |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| A `Tensor`. Has the same type as `x`. ||

<br />

### `__rmul__`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/math_ops.py#L1495-L1501)

    __rmul__(
        x
    )

Dispatches cwise mul for "Dense*Dense" and "Dense*Sparse".

### `__ror__`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/math_ops.py#L1495-L1501)

    __ror__(
        x
    )

### `__rpow__`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/math_ops.py#L1495-L1501)

    __rpow__(
        x
    )

Computes the power of one value to another.

Given a tensor `x` and a tensor `y`, this operation computes \\(x\^y\\) for
corresponding elements in `x` and `y`. For example:

    x = tf.constant([[2, 2], [3, 3]])
    y = tf.constant([[8, 16], [2, 3]])
    tf.pow(x, y)  # [[256, 65536], [9, 27]]

<br />

<br />

<br />

| Args ||
|---|---|
| `x` | A `Tensor` of type `float16`, `float32`, `float64`, `int32`, `int64`, `complex64`, or `complex128`. |
| `y` | A `Tensor` of type `float16`, `float32`, `float64`, `int32`, `int64`, `complex64`, or `complex128`. |
| `name` | A name for the operation (optional). |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| A `Tensor`. ||

<br />

### `__rsub__`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/math_ops.py#L1495-L1501)

    __rsub__(
        x
    )

Returns x - y element-wise.

> [!NOTE]
> **Note:** [`tf.subtract`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/math/subtract) supports broadcasting. More about broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)

Both input and output have a range `(-inf, inf)`.

Example usages below.

Subtract operation between an array and a scalar:

    x = [1, 2, 3, 4, 5]
    y = 1
    tf.subtract(x, y)
    <tf.Tensor: shape=(5,), dtype=int32, numpy=array([0, 1, 2, 3, 4], dtype=int32)>
    tf.subtract(y, x)
    <tf.Tensor: shape=(5,), dtype=int32,
    numpy=array([ 0, -1, -2, -3, -4], dtype=int32)>

Note that binary `-` operator can be used instead:

    x = tf.convert_to_tensor([1, 2, 3, 4, 5])
    y = tf.convert_to_tensor(1)
    x - y
    <tf.Tensor: shape=(5,), dtype=int32, numpy=array([0, 1, 2, 3, 4], dtype=int32)>

Subtract operation between an array and a tensor of same shape:

    x = [1, 2, 3, 4, 5]
    y = tf.constant([5, 4, 3, 2, 1])
    tf.subtract(y, x)
    <tf.Tensor: shape=(5,), dtype=int32,
    numpy=array([ 4,  2,  0, -2, -4], dtype=int32)>

> [!WARNING]
> **Warning:** If one of the inputs (`x` or `y`) is a tensor and the other is a non-tensor, the non-tensor input will adopt (or get casted to) the data type of the tensor input. This can potentially cause unwanted overflow or underflow conversion.

For example,

    x = tf.constant([1, 2], dtype=tf.int8)
    y = [2**8 + 1, 2**8 + 2]
    tf.subtract(x, y)
    <tf.Tensor: shape=(2,), dtype=int8, numpy=array([0, 0], dtype=int8)>

When subtracting two input values of different shapes, [`tf.subtract`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/math/subtract) follows the
[general broadcasting rules](https://numpy.org/doc/stable/user/basics.broadcasting.html#general-broadcasting-rules)
. The two input array shapes are compared element-wise. Starting with the
trailing dimensions, the two dimensions either have to be equal or one of them
needs to be `1`.

For example,

    x = np.ones(6).reshape(2, 3, 1)
    y = np.ones(6).reshape(2, 1, 3)
    tf.subtract(x, y)
    <tf.Tensor: shape=(2, 3, 3), dtype=float64, numpy=
    array([[[0., 0., 0.],
            [0., 0., 0.],
            [0., 0., 0.]],
           [[0., 0., 0.],
            [0., 0., 0.],
            [0., 0., 0.]]])>

Example with inputs of different dimensions:

    x = np.ones(6).reshape(2, 3, 1)
    y = np.ones(6).reshape(1, 6)
    tf.subtract(x, y)
    <tf.Tensor: shape=(2, 3, 6), dtype=float64, numpy=
    array([[[0., 0., 0., 0., 0., 0.],
            [0., 0., 0., 0., 0., 0.],
            [0., 0., 0., 0., 0., 0.]],
           [[0., 0., 0., 0., 0., 0.],
            [0., 0., 0., 0., 0., 0.],
            [0., 0., 0., 0., 0., 0.]]])>

<br />

<br />

<br />

| Args ||
|---|---|
| `x` | A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `uint8`, `int8`, `uint16`, `int16`, `int32`, `int64`, `complex64`, `complex128`, `uint32`, `uint64`. |
| `y` | A `Tensor`. Must have the same type as `x`. |
| `name` | A name for the operation (optional). |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| A `Tensor`. Has the same type as `x`. ||

<br />

### `__rtruediv__`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/math_ops.py#L1495-L1501)

    __rtruediv__(
        x
    )

Divides x / y elementwise (using Python 3 division operator semantics).

> [!NOTE]
> **Note:** Prefer using the Tensor operator or tf.divide which obey Python division operator semantics.

This function forces Python 3 division operator semantics where all integer
arguments are cast to floating types first. This op is generated by normal
`x / y` division in Python 3 and in Python 2.7 with
`from __future__ import division`. If you want integer division that rounds
down, use `x // y` or `tf.math.floordiv`.

`x` and `y` must have the same numeric type. If the inputs are floating
point, the output will have the same type. If the inputs are integral, the
inputs are cast to `float32` for `int8` and `int16` and `float64` for `int32`
and `int64` (matching the behavior of Numpy).

<br />

<br />

<br />

| Args ||
|---|---|
| `x` | `Tensor` numerator of numeric type. |
| `y` | `Tensor` denominator of numeric type. |
| `name` | A name for the operation (optional). |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| `x / y` evaluated in floating point. ||

<br />

<br />

<br />

<br />

| Raises ||
|---|---|
| `TypeError` | If `x` and `y` have different dtypes. |

<br />

### `__rxor__`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/math_ops.py#L1495-L1501)

    __rxor__(
        x
    )

### `__sub__`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/math_ops.py#L1458-L1484)

    __sub__(
        y
    )

Returns x - y element-wise.

> [!NOTE]
> **Note:** [`tf.subtract`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/math/subtract) supports broadcasting. More about broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)

Both input and output have a range `(-inf, inf)`.

Example usages below.

Subtract operation between an array and a scalar:

    x = [1, 2, 3, 4, 5]
    y = 1
    tf.subtract(x, y)
    <tf.Tensor: shape=(5,), dtype=int32, numpy=array([0, 1, 2, 3, 4], dtype=int32)>
    tf.subtract(y, x)
    <tf.Tensor: shape=(5,), dtype=int32,
    numpy=array([ 0, -1, -2, -3, -4], dtype=int32)>

Note that binary `-` operator can be used instead:

    x = tf.convert_to_tensor([1, 2, 3, 4, 5])
    y = tf.convert_to_tensor(1)
    x - y
    <tf.Tensor: shape=(5,), dtype=int32, numpy=array([0, 1, 2, 3, 4], dtype=int32)>

Subtract operation between an array and a tensor of same shape:

    x = [1, 2, 3, 4, 5]
    y = tf.constant([5, 4, 3, 2, 1])
    tf.subtract(y, x)
    <tf.Tensor: shape=(5,), dtype=int32,
    numpy=array([ 4,  2,  0, -2, -4], dtype=int32)>

> [!WARNING]
> **Warning:** If one of the inputs (`x` or `y`) is a tensor and the other is a non-tensor, the non-tensor input will adopt (or get casted to) the data type of the tensor input. This can potentially cause unwanted overflow or underflow conversion.

For example,

    x = tf.constant([1, 2], dtype=tf.int8)
    y = [2**8 + 1, 2**8 + 2]
    tf.subtract(x, y)
    <tf.Tensor: shape=(2,), dtype=int8, numpy=array([0, 0], dtype=int8)>

When subtracting two input values of different shapes, [`tf.subtract`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/math/subtract) follows the
[general broadcasting rules](https://numpy.org/doc/stable/user/basics.broadcasting.html#general-broadcasting-rules)
. The two input array shapes are compared element-wise. Starting with the
trailing dimensions, the two dimensions either have to be equal or one of them
needs to be `1`.

For example,

    x = np.ones(6).reshape(2, 3, 1)
    y = np.ones(6).reshape(2, 1, 3)
    tf.subtract(x, y)
    <tf.Tensor: shape=(2, 3, 3), dtype=float64, numpy=
    array([[[0., 0., 0.],
            [0., 0., 0.],
            [0., 0., 0.]],
           [[0., 0., 0.],
            [0., 0., 0.],
            [0., 0., 0.]]])>

Example with inputs of different dimensions:

    x = np.ones(6).reshape(2, 3, 1)
    y = np.ones(6).reshape(1, 6)
    tf.subtract(x, y)
    <tf.Tensor: shape=(2, 3, 6), dtype=float64, numpy=
    array([[[0., 0., 0., 0., 0., 0.],
            [0., 0., 0., 0., 0., 0.],
            [0., 0., 0., 0., 0., 0.]],
           [[0., 0., 0., 0., 0., 0.],
            [0., 0., 0., 0., 0., 0.],
            [0., 0., 0., 0., 0., 0.]]])>

<br />

<br />

<br />

| Args ||
|---|---|
| `x` | A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `uint8`, `int8`, `uint16`, `int16`, `int32`, `int64`, `complex64`, `complex128`, `uint32`, `uint64`. |
| `y` | A `Tensor`. Must have the same type as `x`. |
| `name` | A name for the operation (optional). |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| A `Tensor`. Has the same type as `x`. ||

<br />

### `__truediv__`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/math_ops.py#L1458-L1484)

    __truediv__(
        y
    )

Divides x / y elementwise (using Python 3 division operator semantics).

> [!NOTE]
> **Note:** Prefer using the Tensor operator or tf.divide which obey Python division operator semantics.

This function forces Python 3 division operator semantics where all integer
arguments are cast to floating types first. This op is generated by normal
`x / y` division in Python 3 and in Python 2.7 with
`from __future__ import division`. If you want integer division that rounds
down, use `x // y` or `tf.math.floordiv`.

`x` and `y` must have the same numeric type. If the inputs are floating
point, the output will have the same type. If the inputs are integral, the
inputs are cast to `float32` for `int8` and `int16` and `float64` for `int32`
and `int64` (matching the behavior of Numpy).

<br />

<br />

<br />

| Args ||
|---|---|
| `x` | `Tensor` numerator of numeric type. |
| `y` | `Tensor` denominator of numeric type. |
| `name` | A name for the operation (optional). |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| `x / y` evaluated in floating point. ||

<br />

<br />

<br />

<br />

| Raises ||
|---|---|
| `TypeError` | If `x` and `y` have different dtypes. |

<br />

### `__xor__`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/math_ops.py#L1458-L1484)

    __xor__(
        y
    )