tfra.dynamic_embedding.TrainableWrapper

Class `TrainableWrapper`

This class is a trainable wrapper of Dynamic Embedding,

and the key role is recording the map relation between params and ids. inheriting from the ResourceVariable make it trainable.

`init`

View source

__init__(
    params,
    ids,
    max_norm,
    *args,
    **kwargs
)

Creates an empty TrainableWrapper object.©

Creates a group of tables placed on devices, the type of its keys and values are specified by key_dtype and value_dtype, respectively.

Args:

params: A dynamic_embedding.Variable instance.
ids: A tensor with any shape as same dtype of params.key_dtype.
max_norm: If not None, each values is clipped if its l2-norm is larger than this value. other parameters is same with ResourceVariable.

Returns:

A TrainableWrapper object which is a subclass of ResourceVariable.

Child Classes

class SaveSliceInfo

Properties

`aggregation`

`constraint`

Returns the constraint function associated with this variable.

Returns:

The constraint function that was passed to the variable constructor. Can be None if no constraint was passed.

`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.

`shape`

The shape of this variable.

`synchronization`

`trainable`

Methods

`abs`

__abs__(
    x,
    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]])>

Args:

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

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.

`add`

__add__(
    a,
    *args,
    **kwargs
)

The operation invoked by the Tensor.__add__ operator.

Purpose in the API:

This method is exposed in TensorFlow's API so that library developers
can register dispatching for `Tensor.__add__` to allow it to handle
custom composite tensors & other custom objects.

The API symbol is not intended to be called by users directly and does
appear in TensorFlow's generated documentation.

Args:

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

Returns:

The result of the elementwise + operation.

`and`

__and__(
    a,
    *args,
    **kwargs
)

`bool`

__bool__()

`div`

__div__(
    a,
    *args,
    **kwargs
)

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

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Deprecated in favor of operator or tf.math.divide.

NOTE: Prefer using the Tensor division operator or tf.divide which obey Python 3 division operator semantics.

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.

Args:

x: Tensor numerator of real numeric type.
y: Tensor denominator of real numeric type.
name: A name for the operation (optional).

Returns:

x / y returns the quotient of x and y.

`eq`

__eq__(other)

Compares two variables element-wise for equality.

`floordiv`

__floordiv__(
    a,
    *args,
    **kwargs
)

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

The same as tf.compat.v1.div(x,y) for integers, but uses tf.floor(tf.compat.v1.div(x,y)) for floating point arguments so that the result is always an integer (though possibly an integer represented as floating point). This op is generated by x // y floor division in Python 3 and in Python 2.7 with from __future__ import division.

x and y must have the same type, and the result will have the same type as well.

Args:

x: Tensor numerator of real numeric type.
y: Tensor denominator of real numeric type.
name: A name for the operation (optional).

Returns:

x / y rounded down.

Raises:

TypeError: If the inputs are complex.

`ge`

__ge__(
    x,
    y,
    name=None
)

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

NOTE: math.greater_equal supports broadcasting. More about broadcasting here

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]

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).

Returns:

A Tensor of type bool.

`getitem`

__getitem__(
    var,
    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__ 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(). For example,

import tensorflow as tf
A = tf.Variable([[1,2,3], [4,5,6], [7,8,9]], dtype=tf.float32)
with tf.compat.v1.Session() as sess:
  sess.run(tf.compat.v1.global_variables_initializer())
  print(sess.run(A[:2, :2]))  # => [[1,2], [4,5]]

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

Note that assignments currently do not support NumPy broadcasting semantics.

Args:

var: An ops.Variable object.
slice_spec: The arguments to Tensor.__getitem__.

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.

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.

`gt`

__gt__(
    x,
    y,
    name=None
)

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

NOTE: math.greater supports broadcasting. More about broadcasting here

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]

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).

Returns:

A Tensor of type bool.

`invert`

__invert__(
    a,
    *args,
    **kwargs
)

`iter`

__iter__()

Dummy method to prevent iteration.

Do not call.

NOTE(mrry): If we register getitem as an overloaded operator, Python will valiantly attempt to iterate over the variable's Tensor from 0 to infinity. Declaring this method prevents this unintended behavior.

Raises:

TypeError: when invoked.

`le`

__le__(
    x,
    y,
    name=None
)

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

NOTE: math.less_equal supports broadcasting. More about broadcasting here

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]

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).

Returns:

A Tensor of type bool.

`lt`

__lt__(
    x,
    y,
    name=None
)

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

NOTE: math.less supports broadcasting. More about broadcasting here

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]

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).

Returns:

A Tensor of type bool.

`matmul`

__matmul__(
    a,
    *args,
    **kwargs
)

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: float16, float32, float64, int32, 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). In TensorFlow, it simply calls the tf.matmul() function, so the following lines are equivalent:

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

Args:

a: tf.Tensor of type float16, float32, float64, int32, complex64, complex128 and rank > 1.
b: 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, it just makes optimizations that assume most values in a are zero. See tf.sparse.sparse_dense_matmul for some support for tf.sparse.SparseTensor multiplication.
b_is_sparse: If True, b is treated as a sparse matrix. Notice, this does not support tf.sparse.SparseTensor, it just makes optimizations that assume most values in a are zero. See tf.sparse.sparse_dense_matmul for some support for tf.sparse.SparseTensor multiplication.
name: Name for the operation (optional).

Returns:

A 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:

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.

Raises:

ValueError: If transpose_a and adjoint_a, or transpose_b and adjoint_b are both set to True.

`mod`

__mod__(
    a,
    *args,
    **kwargs
)

Returns element-wise remainder of division. When x < 0 xor y < 0 is

true, this follows Python semantics in that the result here is consistent with a flooring divide. E.g. floor(x / y) * y + mod(x, y) = x.

NOTE: math.floormod supports broadcasting. More about broadcasting here

Args:

x: A Tensor. Must be one of the following types: int32, int64, uint64, bfloat16, half, float32, float64.
y: A Tensor. Must have the same type as x.
name: A name for the operation (optional).

Returns:

A Tensor. Has the same type as x.

`mul`

__mul__(
    a,
    *args,
    **kwargs
)

Dispatches cwise mul for "DenseDense" and "DenseSparse".

`ne`

__ne__(other)

Compares two variables element-wise for equality.

`neg`

__neg__(
    a,
    *args,
    **kwargs
)

Computes numerical negative value element-wise.

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

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).

Returns:

A Tensor. Has the same type as x.

`nonzero`

__nonzero__()

`or`

__or__(
    a,
    *args,
    **kwargs
)

`pow`

__pow__(
    a,
    *args,
    **kwargs
)

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]]

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).

Returns:

A Tensor.

`radd`

__radd__(
    a,
    *args,
    **kwargs
)

The operation invoked by the Tensor.__add__ operator.

Purpose in the API:

This method is exposed in TensorFlow's API so that library developers
can register dispatching for `Tensor.__add__` to allow it to handle
custom composite tensors & other custom objects.

The API symbol is not intended to be called by users directly and does
appear in TensorFlow's generated documentation.

Args:

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

Returns:

The result of the elementwise + operation.

`rand`

__rand__(
    a,
    *args,
    **kwargs
)

`rdiv`

__rdiv__(
    a,
    *args,
    **kwargs
)

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

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Deprecated in favor of operator or tf.math.divide.

NOTE: Prefer using the Tensor division operator or tf.divide which obey Python 3 division operator semantics.

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.

Args:

x: Tensor numerator of real numeric type.
y: Tensor denominator of real numeric type.
name: A name for the operation (optional).

Returns:

x / y returns the quotient of x and y.

`rfloordiv`

__rfloordiv__(
    a,
    *args,
    **kwargs
)

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

The same as tf.compat.v1.div(x,y) for integers, but uses tf.floor(tf.compat.v1.div(x,y)) for floating point arguments so that the result is always an integer (though possibly an integer represented as floating point). This op is generated by x // y floor division in Python 3 and in Python 2.7 with from __future__ import division.

x and y must have the same type, and the result will have the same type as well.

Args:

x: Tensor numerator of real numeric type.
y: Tensor denominator of real numeric type.
name: A name for the operation (optional).

Returns:

x / y rounded down.

Raises:

TypeError: If the inputs are complex.

`rmatmul`

__rmatmul__(
    a,
    *args,
    **kwargs
)

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: float16, float32, float64, int32, 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). In TensorFlow, it simply calls the tf.matmul() function, so the following lines are equivalent:

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

Args:

a: tf.Tensor of type float16, float32, float64, int32, complex64, complex128 and rank > 1.
b: 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, it just makes optimizations that assume most values in a are zero. See tf.sparse.sparse_dense_matmul for some support for tf.sparse.SparseTensor multiplication.
b_is_sparse: If True, b is treated as a sparse matrix. Notice, this does not support tf.sparse.SparseTensor, it just makes optimizations that assume most values in a are zero. See tf.sparse.sparse_dense_matmul for some support for tf.sparse.SparseTensor multiplication.
name: Name for the operation (optional).

Returns:

A 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:

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.

Raises:

ValueError: If transpose_a and adjoint_a, or transpose_b and adjoint_b are both set to True.

`rmod`

__rmod__(
    a,
    *args,
    **kwargs
)

Returns element-wise remainder of division. When x < 0 xor y < 0 is

true, this follows Python semantics in that the result here is consistent with a flooring divide. E.g. floor(x / y) * y + mod(x, y) = x.

NOTE: math.floormod supports broadcasting. More about broadcasting here

Args:

x: A Tensor. Must be one of the following types: int32, int64, uint64, bfloat16, half, float32, float64.
y: A Tensor. Must have the same type as x.
name: A name for the operation (optional).

Returns:

A Tensor. Has the same type as x.

`rmul`

__rmul__(
    a,
    *args,
    **kwargs
)

Dispatches cwise mul for "DenseDense" and "DenseSparse".

`ror`

__ror__(
    a,
    *args,
    **kwargs
)

`rpow`

__rpow__(
    a,
    *args,
    **kwargs
)

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]]

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).

Returns:

A Tensor.

`rsub`

__rsub__(
    a,
    *args,
    **kwargs
)

Returns x - y element-wise.

NOTE: tf.subtract supports broadcasting. More about broadcasting here

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: 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 follows the 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.]]])>

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).

Returns:

A Tensor. Has the same type as x.

`rtruediv`

__rtruediv__(
    a,
    *args,
    **kwargs
)

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

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).

Args:

x: Tensor numerator of numeric type.
y: Tensor denominator of numeric type.
name: A name for the operation (optional).

Returns:

x / y evaluated in floating point.

Raises:

TypeError: If x and y have different dtypes.

`rxor`

__rxor__(
    a,
    *args,
    **kwargs
)

`sub`

__sub__(
    a,
    *args,
    **kwargs
)

Returns x - y element-wise.

NOTE: tf.subtract supports broadcasting. More about broadcasting here

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: 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 follows the 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.]]])>

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).

Returns:

A Tensor. Has the same type as x.

`truediv`

__truediv__(
    a,
    *args,
    **kwargs
)

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

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).

Args:

x: Tensor numerator of numeric type.
y: Tensor denominator of numeric type.
name: A name for the operation (optional).

Returns:

x / y evaluated in floating point.

Raises:

TypeError: If x and y have different dtypes.

`xor`

__xor__(
    a,
    *args,
    **kwargs
)

`assign`

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

Assigns a new value to this variable.

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.

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.

`assign_add`

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

Adds a value to this variable.

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.

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.

`assign_sub`

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

Subtracts a value from this variable.

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.

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.

`batch_scatter_update`

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

Assigns 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.

Args:

sparse_delta: tf.IndexedSlices to be assigned to this variable.
use_locking: If True, use locking during the operation.
name: the name of the operation.

Returns:

The updated variable.

Raises:

TypeError: if sparse_delta is not an IndexedSlices.

`count_up_to`

count_up_to(limit)

Increments this variable until it reaches limit. (deprecated)

Warning: 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).

Args:

limit: value at which incrementing the variable raises an error.

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.

`eval`

eval(session=None)

Evaluates and returns the value of this variable.

`experimental_ref`

experimental_ref()

DEPRECATED FUNCTION

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use ref() instead.

`from_proto`

@staticmethod
from_proto(
    variable_def,
    import_scope=None
)

Returns a Variable object created from variable_def.

`gather_nd`

gather_nd(
    indices,
    name=None
)

Reads the value of this variable sparsely, using gather_nd.

`get_shape`

get_shape()

Alias of Variable.shape.

`initialized_value`

initialized_value()

Returns the value of the initialized variable. (deprecated)

Warning: 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)

Returns:

A Tensor holding the value of this variable after its initializer has run.

`is_initialized`

is_initialized(name=None)

Checks whether a resource variable has been initialized.

Outputs boolean scalar indicating whether the tensor has been initialized.

Args:

name: A name for the operation (optional).

Returns:

A Tensor of type bool.

`load`

load(
    value,
    session=None
)

Load new value into this variable. (deprecated)

Warning: 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 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]

Args:

value: New variable value
session: The session to use to evaluate this variable. If none, the default session is used.

Raises:

ValueError: Session is not passed and no default session

`numpy`

numpy()

`prefetch_values`

View source

prefetch_values(update=(False))

`read_value`

View source

read_value(do_prefetch=(True))

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. Args: do_prefetch: get value from params before reading, if True

Returns:

the read operation.

`ref`

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`

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

Adds tf.IndexedSlices to this variable.

Args:

sparse_delta: tf.IndexedSlices to be added to this variable.
use_locking: If True, use locking during the operation.
name: the name of the operation.

Returns:

The updated variable.

Raises:

TypeError: if sparse_delta is not an IndexedSlices.

`scatter_div`

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

Divide this variable by tf.IndexedSlices.

Args:

sparse_delta: tf.IndexedSlices to divide this variable by.
use_locking: If True, use locking during the operation.
name: the name of the operation.

Returns:

The updated variable.

Raises:

TypeError: if sparse_delta is not an IndexedSlices.

`scatter_max`

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

Updates this variable with the max of tf.IndexedSlices and itself.

Args:

sparse_delta: 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.

Returns:

The updated variable.

Raises:

TypeError: if sparse_delta is not an IndexedSlices.

`scatter_min`

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

Updates this variable with the min of tf.IndexedSlices and itself.

Args:

sparse_delta: 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.

Returns:

The updated variable.

Raises:

TypeError: if sparse_delta is not an IndexedSlices.

`scatter_mul`

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

Multiply this variable by tf.IndexedSlices.

Args:

sparse_delta: tf.IndexedSlices to multiply this variable by.
use_locking: If True, use locking during the operation.
name: the name of the operation.

Returns:

The updated variable.

Raises:

TypeError: if sparse_delta is not an IndexedSlices.

`scatter_nd_add`

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 Kth 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 for more details about how to make updates to slices.

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.

Returns:

The updated variable.

`scatter_nd_max`

scatter_nd_max(
    indices,
    updates,
    name=None
)

Updates this variable with the max of 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 Kth 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 for more details about how to make updates to slices.

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.

Returns:

The updated variable.

`scatter_nd_min`

scatter_nd_min(
    indices,
    updates,
    name=None
)

Updates this variable with the min of 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 Kth 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 for more details about how to make updates to slices.

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.

Returns:

The updated variable.

`scatter_nd_sub`

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 Kth 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 for more details about how to make updates to slices.

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.

Returns:

The updated variable.

`scatter_nd_update`

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 Kth 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 for more details about how to make updates to slices.

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.

Returns:

The updated variable.

`scatter_sub`

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

Subtracts tf.IndexedSlices from this variable.

Args:

sparse_delta: tf.IndexedSlices to be subtracted from this variable.
use_locking: If True, use locking during the operation.
name: the name of the operation.

Returns:

The updated variable.

Raises:

TypeError: if sparse_delta is not an IndexedSlices.

`scatter_update`

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

Assigns tf.IndexedSlices to this variable.

Args:

sparse_delta: tf.IndexedSlices to be assigned to this variable.
use_locking: If True, use locking during the operation.
name: the name of the operation.

Returns:

The updated variable.

Raises:

TypeError: if sparse_delta is not an IndexedSlices.

`set_shape`

set_shape(shape)

Overrides the shape for this variable.

Args:

shape: the TensorShape representing the overridden shape.

`size`

View source

size()

`sparse_read`

sparse_read(
    indices,
    name=None
)

Reads the value of this variable sparsely, using gather.

`to_proto`

to_proto(export_scope=None)

Converts a ResourceVariable to a VariableDef protocol buffer.

Args:

export_scope: Optional string. Name scope to remove.

Raises:

RuntimeError: If run in EAGER mode.

Returns:

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

`transform`

View source

transform(result)

`update_op`

View source

update_op(v0=None)

`value`

value()

A cached operation which reads the value of this variable.

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TrainableWrapper.md

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tfra.dynamic_embedding.TrainableWrapper

Class TrainableWrapper

__init__

Args:

Returns:

Child Classes

Properties

aggregation

constraint

Returns:

create

device

dtype

graph

handle

initial_value

initializer

name

op

shape

synchronization

trainable

Methods

__abs__

For example:

Args:

Returns:

__add__

Args:

Returns:

__and__

__bool__

__div__

Args:

Returns:

__eq__

__floordiv__

Args:

Returns:

Raises:

__ge__

Example:

Args:

Returns:

__getitem__

Args:

Returns:

Raises:

__gt__

Example:

Args:

Returns:

__invert__

__iter__

Raises:

__le__

Example:

Args:

Returns:

__lt__

Example:

Args:

Returns:

__matmul__

Args:

Returns:

Raises:

__mod__

Args:

Returns:

__mul__

__ne__

__neg__

Args:

Class `TrainableWrapper`

`init`

`aggregation`

`constraint`

`create`

`device`

`dtype`

`graph`

`handle`

`initial_value`

`initializer`

`name`

`op`

`shape`

`synchronization`

`trainable`

`abs`

`add`

`and`

`bool`

`div`

`eq`

`floordiv`

`ge`

`getitem`

`gt`

`invert`

`iter`

`le`

`lt`

`matmul`

`mod`

`mul`

`ne`

`neg`

`nonzero`

`or`

`pow`

`radd`

`rand`

`rdiv`

`rfloordiv`

`rmatmul`

`rmod`

`rmul`

`ror`

`rpow`

`rsub`

`rtruediv`

`rxor`

`sub`

`truediv`

`xor`

`assign`

`assign_add`

`assign_sub`

`batch_scatter_update`

`count_up_to`

`eval`

`experimental_ref`