# LostTech.TensorFlow : API Documentation

Type tf.sets

Namespace tensorflow

### Public static methods

#### SparseTensordifference(IGraphNodeBase a, object b, bool aminusb, bool validate_indices)

Compute set difference of elements in last dimension of `a` and `b`.

All but the last dimension of `a` and `b` must match.

Example:
##### Parameters
IGraphNodeBase a
`Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices must be sorted in row-major order.
object b
`Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices must be sorted in row-major order.
bool aminusb
Whether to subtract `b` from `a`, vs vice versa.
bool validate_indices
Whether to validate the order and range of sparse indices in `a` and `b`.
##### Returns
SparseTensor
A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but the last dimension the same. Elements along the last dimension contain the differences.
Show Example
import tensorflow as tf
import collections

# Represent the following array of sets as a sparse tensor: # a = np.array([[{1, 2}, {3}], [{4}, {5, 6}]]) a = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 2), ((0, 1, 0), 3), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ]) a = tf.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2, 2, 2])

# np.array([[{1, 3}, {2}], [{4, 5}, {5, 6, 7, 8}]]) b = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 3), ((0, 1, 0), 2), ((1, 0, 0), 4), ((1, 0, 1), 5), ((1, 1, 0), 5), ((1, 1, 1), 6), ((1, 1, 2), 7), ((1, 1, 3), 8), ]) b = tf.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])

# `set_difference` is applied to each aligned pair of sets. tf.sets.difference(a, b)

# The result will be equivalent to either of: # # np.array([[{2}, {3}], [{}, {}]]) # # collections.OrderedDict([ # ((0, 0, 0), 2), # ((0, 1, 0), 3), # ])

#### SparseTensordifference(IGraphNodeBase a, IEnumerable<object> b, bool aminusb, bool validate_indices)

Compute set difference of elements in last dimension of `a` and `b`.

All but the last dimension of `a` and `b` must match.

Example:
##### Parameters
IGraphNodeBase a
`Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices must be sorted in row-major order.
IEnumerable<object> b
`Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices must be sorted in row-major order.
bool aminusb
Whether to subtract `b` from `a`, vs vice versa.
bool validate_indices
Whether to validate the order and range of sparse indices in `a` and `b`.
##### Returns
SparseTensor
A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but the last dimension the same. Elements along the last dimension contain the differences.
Show Example
import tensorflow as tf
import collections

# Represent the following array of sets as a sparse tensor: # a = np.array([[{1, 2}, {3}], [{4}, {5, 6}]]) a = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 2), ((0, 1, 0), 3), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ]) a = tf.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2, 2, 2])

# np.array([[{1, 3}, {2}], [{4, 5}, {5, 6, 7, 8}]]) b = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 3), ((0, 1, 0), 2), ((1, 0, 0), 4), ((1, 0, 1), 5), ((1, 1, 0), 5), ((1, 1, 1), 6), ((1, 1, 2), 7), ((1, 1, 3), 8), ]) b = tf.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])

# `set_difference` is applied to each aligned pair of sets. tf.sets.difference(a, b)

# The result will be equivalent to either of: # # np.array([[{2}, {3}], [{}, {}]]) # # collections.OrderedDict([ # ((0, 0, 0), 2), # ((0, 1, 0), 3), # ])

#### SparseTensordifference(IGraphNodeBase a, ndarray b, bool aminusb, bool validate_indices)

Compute set difference of elements in last dimension of `a` and `b`.

All but the last dimension of `a` and `b` must match.

Example:
##### Parameters
IGraphNodeBase a
`Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices must be sorted in row-major order.
ndarray b
`Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices must be sorted in row-major order.
bool aminusb
Whether to subtract `b` from `a`, vs vice versa.
bool validate_indices
Whether to validate the order and range of sparse indices in `a` and `b`.
##### Returns
SparseTensor
A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but the last dimension the same. Elements along the last dimension contain the differences.
Show Example
import tensorflow as tf
import collections

# Represent the following array of sets as a sparse tensor: # a = np.array([[{1, 2}, {3}], [{4}, {5, 6}]]) a = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 2), ((0, 1, 0), 3), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ]) a = tf.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2, 2, 2])

# np.array([[{1, 3}, {2}], [{4, 5}, {5, 6, 7, 8}]]) b = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 3), ((0, 1, 0), 2), ((1, 0, 0), 4), ((1, 0, 1), 5), ((1, 1, 0), 5), ((1, 1, 1), 6), ((1, 1, 2), 7), ((1, 1, 3), 8), ]) b = tf.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])

# `set_difference` is applied to each aligned pair of sets. tf.sets.difference(a, b)

# The result will be equivalent to either of: # # np.array([[{2}, {3}], [{}, {}]]) # # collections.OrderedDict([ # ((0, 0, 0), 2), # ((0, 1, 0), 3), # ])

#### SparseTensordifference(IndexedSlices a, object b, bool aminusb, bool validate_indices)

Compute set difference of elements in last dimension of `a` and `b`.

All but the last dimension of `a` and `b` must match.

Example:
##### Parameters
IndexedSlices a
`Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices must be sorted in row-major order.
object b
`Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices must be sorted in row-major order.
bool aminusb
Whether to subtract `b` from `a`, vs vice versa.
bool validate_indices
Whether to validate the order and range of sparse indices in `a` and `b`.
##### Returns
SparseTensor
A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but the last dimension the same. Elements along the last dimension contain the differences.
Show Example
import tensorflow as tf
import collections

# Represent the following array of sets as a sparse tensor: # a = np.array([[{1, 2}, {3}], [{4}, {5, 6}]]) a = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 2), ((0, 1, 0), 3), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ]) a = tf.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2, 2, 2])

# np.array([[{1, 3}, {2}], [{4, 5}, {5, 6, 7, 8}]]) b = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 3), ((0, 1, 0), 2), ((1, 0, 0), 4), ((1, 0, 1), 5), ((1, 1, 0), 5), ((1, 1, 1), 6), ((1, 1, 2), 7), ((1, 1, 3), 8), ]) b = tf.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])

# `set_difference` is applied to each aligned pair of sets. tf.sets.difference(a, b)

# The result will be equivalent to either of: # # np.array([[{2}, {3}], [{}, {}]]) # # collections.OrderedDict([ # ((0, 0, 0), 2), # ((0, 1, 0), 3), # ])

#### SparseTensordifference(IndexedSlices a, IGraphNodeBase b, bool aminusb, bool validate_indices)

Compute set difference of elements in last dimension of `a` and `b`.

All but the last dimension of `a` and `b` must match.

Example:
##### Parameters
IndexedSlices a
`Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices must be sorted in row-major order.
IGraphNodeBase b
`Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices must be sorted in row-major order.
bool aminusb
Whether to subtract `b` from `a`, vs vice versa.
bool validate_indices
Whether to validate the order and range of sparse indices in `a` and `b`.
##### Returns
SparseTensor
A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but the last dimension the same. Elements along the last dimension contain the differences.
Show Example
import tensorflow as tf
import collections

# Represent the following array of sets as a sparse tensor: # a = np.array([[{1, 2}, {3}], [{4}, {5, 6}]]) a = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 2), ((0, 1, 0), 3), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ]) a = tf.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2, 2, 2])

# np.array([[{1, 3}, {2}], [{4, 5}, {5, 6, 7, 8}]]) b = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 3), ((0, 1, 0), 2), ((1, 0, 0), 4), ((1, 0, 1), 5), ((1, 1, 0), 5), ((1, 1, 1), 6), ((1, 1, 2), 7), ((1, 1, 3), 8), ]) b = tf.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])

# `set_difference` is applied to each aligned pair of sets. tf.sets.difference(a, b)

# The result will be equivalent to either of: # # np.array([[{2}, {3}], [{}, {}]]) # # collections.OrderedDict([ # ((0, 0, 0), 2), # ((0, 1, 0), 3), # ])

#### SparseTensordifference(IndexedSlices a, ValueTuple<IEnumerable<object>, object> b, bool aminusb, bool validate_indices)

Compute set difference of elements in last dimension of `a` and `b`.

All but the last dimension of `a` and `b` must match.

Example:
##### Parameters
IndexedSlices a
`Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices must be sorted in row-major order.
ValueTuple<IEnumerable<object>, object> b
`Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices must be sorted in row-major order.
bool aminusb
Whether to subtract `b` from `a`, vs vice versa.
bool validate_indices
Whether to validate the order and range of sparse indices in `a` and `b`.
##### Returns
SparseTensor
A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but the last dimension the same. Elements along the last dimension contain the differences.
Show Example
import tensorflow as tf
import collections

# Represent the following array of sets as a sparse tensor: # a = np.array([[{1, 2}, {3}], [{4}, {5, 6}]]) a = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 2), ((0, 1, 0), 3), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ]) a = tf.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2, 2, 2])

# np.array([[{1, 3}, {2}], [{4, 5}, {5, 6, 7, 8}]]) b = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 3), ((0, 1, 0), 2), ((1, 0, 0), 4), ((1, 0, 1), 5), ((1, 1, 0), 5), ((1, 1, 1), 6), ((1, 1, 2), 7), ((1, 1, 3), 8), ]) b = tf.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])

# `set_difference` is applied to each aligned pair of sets. tf.sets.difference(a, b)

# The result will be equivalent to either of: # # np.array([[{2}, {3}], [{}, {}]]) # # collections.OrderedDict([ # ((0, 0, 0), 2), # ((0, 1, 0), 3), # ])

#### SparseTensordifference(IndexedSlices a, IEnumerable<object> b, bool aminusb, bool validate_indices)

Compute set difference of elements in last dimension of `a` and `b`.

All but the last dimension of `a` and `b` must match.

Example:
##### Parameters
IndexedSlices a
`Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices must be sorted in row-major order.
IEnumerable<object> b
`Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices must be sorted in row-major order.
bool aminusb
Whether to subtract `b` from `a`, vs vice versa.
bool validate_indices
Whether to validate the order and range of sparse indices in `a` and `b`.
##### Returns
SparseTensor
A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but the last dimension the same. Elements along the last dimension contain the differences.
Show Example
import tensorflow as tf
import collections

# Represent the following array of sets as a sparse tensor: # a = np.array([[{1, 2}, {3}], [{4}, {5, 6}]]) a = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 2), ((0, 1, 0), 3), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ]) a = tf.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2, 2, 2])

# np.array([[{1, 3}, {2}], [{4, 5}, {5, 6, 7, 8}]]) b = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 3), ((0, 1, 0), 2), ((1, 0, 0), 4), ((1, 0, 1), 5), ((1, 1, 0), 5), ((1, 1, 1), 6), ((1, 1, 2), 7), ((1, 1, 3), 8), ]) b = tf.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])

# `set_difference` is applied to each aligned pair of sets. tf.sets.difference(a, b)

# The result will be equivalent to either of: # # np.array([[{2}, {3}], [{}, {}]]) # # collections.OrderedDict([ # ((0, 0, 0), 2), # ((0, 1, 0), 3), # ])

#### SparseTensordifference(IndexedSlices a, ndarray b, bool aminusb, bool validate_indices)

Compute set difference of elements in last dimension of `a` and `b`.

All but the last dimension of `a` and `b` must match.

Example:
##### Parameters
IndexedSlices a
`Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices must be sorted in row-major order.
ndarray b
`Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices must be sorted in row-major order.
bool aminusb
Whether to subtract `b` from `a`, vs vice versa.
bool validate_indices
Whether to validate the order and range of sparse indices in `a` and `b`.
##### Returns
SparseTensor
A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but the last dimension the same. Elements along the last dimension contain the differences.
Show Example
import tensorflow as tf
import collections

# Represent the following array of sets as a sparse tensor: # a = np.array([[{1, 2}, {3}], [{4}, {5, 6}]]) a = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 2), ((0, 1, 0), 3), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ]) a = tf.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2, 2, 2])

# np.array([[{1, 3}, {2}], [{4, 5}, {5, 6, 7, 8}]]) b = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 3), ((0, 1, 0), 2), ((1, 0, 0), 4), ((1, 0, 1), 5), ((1, 1, 0), 5), ((1, 1, 1), 6), ((1, 1, 2), 7), ((1, 1, 3), 8), ]) b = tf.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])

# `set_difference` is applied to each aligned pair of sets. tf.sets.difference(a, b)

# The result will be equivalent to either of: # # np.array([[{2}, {3}], [{}, {}]]) # # collections.OrderedDict([ # ((0, 0, 0), 2), # ((0, 1, 0), 3), # ])

#### SparseTensordifference(ValueTuple<PythonClassContainer, PythonClassContainer> a, object b, bool aminusb, bool validate_indices)

Compute set difference of elements in last dimension of `a` and `b`.

All but the last dimension of `a` and `b` must match.

Example:
##### Parameters
ValueTuple<PythonClassContainer, PythonClassContainer> a
`Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices must be sorted in row-major order.
object b
`Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices must be sorted in row-major order.
bool aminusb
Whether to subtract `b` from `a`, vs vice versa.
bool validate_indices
Whether to validate the order and range of sparse indices in `a` and `b`.
##### Returns
SparseTensor
A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but the last dimension the same. Elements along the last dimension contain the differences.
Show Example
import tensorflow as tf
import collections

# Represent the following array of sets as a sparse tensor: # a = np.array([[{1, 2}, {3}], [{4}, {5, 6}]]) a = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 2), ((0, 1, 0), 3), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ]) a = tf.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2, 2, 2])

# np.array([[{1, 3}, {2}], [{4, 5}, {5, 6, 7, 8}]]) b = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 3), ((0, 1, 0), 2), ((1, 0, 0), 4), ((1, 0, 1), 5), ((1, 1, 0), 5), ((1, 1, 1), 6), ((1, 1, 2), 7), ((1, 1, 3), 8), ]) b = tf.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])

# `set_difference` is applied to each aligned pair of sets. tf.sets.difference(a, b)

# The result will be equivalent to either of: # # np.array([[{2}, {3}], [{}, {}]]) # # collections.OrderedDict([ # ((0, 0, 0), 2), # ((0, 1, 0), 3), # ])

#### SparseTensordifference(ValueTuple<PythonClassContainer, PythonClassContainer> a, IGraphNodeBase b, bool aminusb, bool validate_indices)

Compute set difference of elements in last dimension of `a` and `b`.

All but the last dimension of `a` and `b` must match.

Example:
##### Parameters
ValueTuple<PythonClassContainer, PythonClassContainer> a
`Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices must be sorted in row-major order.
IGraphNodeBase b
`Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices must be sorted in row-major order.
bool aminusb
Whether to subtract `b` from `a`, vs vice versa.
bool validate_indices
Whether to validate the order and range of sparse indices in `a` and `b`.
##### Returns
SparseTensor
A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but the last dimension the same. Elements along the last dimension contain the differences.
Show Example
import tensorflow as tf
import collections

# Represent the following array of sets as a sparse tensor: # a = np.array([[{1, 2}, {3}], [{4}, {5, 6}]]) a = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 2), ((0, 1, 0), 3), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ]) a = tf.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2, 2, 2])

# np.array([[{1, 3}, {2}], [{4, 5}, {5, 6, 7, 8}]]) b = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 3), ((0, 1, 0), 2), ((1, 0, 0), 4), ((1, 0, 1), 5), ((1, 1, 0), 5), ((1, 1, 1), 6), ((1, 1, 2), 7), ((1, 1, 3), 8), ]) b = tf.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])

# `set_difference` is applied to each aligned pair of sets. tf.sets.difference(a, b)

# The result will be equivalent to either of: # # np.array([[{2}, {3}], [{}, {}]]) # # collections.OrderedDict([ # ((0, 0, 0), 2), # ((0, 1, 0), 3), # ])

#### SparseTensordifference(ValueTuple<PythonClassContainer, PythonClassContainer> a, ValueTuple<IEnumerable<object>, object> b, bool aminusb, bool validate_indices)

Compute set difference of elements in last dimension of `a` and `b`.

All but the last dimension of `a` and `b` must match.

Example:
##### Parameters
ValueTuple<PythonClassContainer, PythonClassContainer> a
`Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices must be sorted in row-major order.
ValueTuple<IEnumerable<object>, object> b
`Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices must be sorted in row-major order.
bool aminusb
Whether to subtract `b` from `a`, vs vice versa.
bool validate_indices
Whether to validate the order and range of sparse indices in `a` and `b`.
##### Returns
SparseTensor
A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but the last dimension the same. Elements along the last dimension contain the differences.
Show Example
import tensorflow as tf
import collections

# Represent the following array of sets as a sparse tensor: # a = np.array([[{1, 2}, {3}], [{4}, {5, 6}]]) a = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 2), ((0, 1, 0), 3), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ]) a = tf.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2, 2, 2])

# np.array([[{1, 3}, {2}], [{4, 5}, {5, 6, 7, 8}]]) b = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 3), ((0, 1, 0), 2), ((1, 0, 0), 4), ((1, 0, 1), 5), ((1, 1, 0), 5), ((1, 1, 1), 6), ((1, 1, 2), 7), ((1, 1, 3), 8), ]) b = tf.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])

# `set_difference` is applied to each aligned pair of sets. tf.sets.difference(a, b)

# The result will be equivalent to either of: # # np.array([[{2}, {3}], [{}, {}]]) # # collections.OrderedDict([ # ((0, 0, 0), 2), # ((0, 1, 0), 3), # ])

#### SparseTensordifference(ValueTuple<PythonClassContainer, PythonClassContainer> a, IEnumerable<object> b, bool aminusb, bool validate_indices)

Compute set difference of elements in last dimension of `a` and `b`.

All but the last dimension of `a` and `b` must match.

Example:
##### Parameters
ValueTuple<PythonClassContainer, PythonClassContainer> a
`Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices must be sorted in row-major order.
IEnumerable<object> b
`Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices must be sorted in row-major order.
bool aminusb
Whether to subtract `b` from `a`, vs vice versa.
bool validate_indices
Whether to validate the order and range of sparse indices in `a` and `b`.
##### Returns
SparseTensor
A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but the last dimension the same. Elements along the last dimension contain the differences.
Show Example
import tensorflow as tf
import collections

# Represent the following array of sets as a sparse tensor: # a = np.array([[{1, 2}, {3}], [{4}, {5, 6}]]) a = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 2), ((0, 1, 0), 3), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ]) a = tf.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2, 2, 2])

# np.array([[{1, 3}, {2}], [{4, 5}, {5, 6, 7, 8}]]) b = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 3), ((0, 1, 0), 2), ((1, 0, 0), 4), ((1, 0, 1), 5), ((1, 1, 0), 5), ((1, 1, 1), 6), ((1, 1, 2), 7), ((1, 1, 3), 8), ]) b = tf.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])

# `set_difference` is applied to each aligned pair of sets. tf.sets.difference(a, b)

# The result will be equivalent to either of: # # np.array([[{2}, {3}], [{}, {}]]) # # collections.OrderedDict([ # ((0, 0, 0), 2), # ((0, 1, 0), 3), # ])

#### SparseTensordifference(ValueTuple<PythonClassContainer, PythonClassContainer> a, ndarray b, bool aminusb, bool validate_indices)

Compute set difference of elements in last dimension of `a` and `b`.

All but the last dimension of `a` and `b` must match.

Example:
##### Parameters
ValueTuple<PythonClassContainer, PythonClassContainer> a
`Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices must be sorted in row-major order.
ndarray b
`Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices must be sorted in row-major order.
bool aminusb
Whether to subtract `b` from `a`, vs vice versa.
bool validate_indices
Whether to validate the order and range of sparse indices in `a` and `b`.
##### Returns
SparseTensor
A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but the last dimension the same. Elements along the last dimension contain the differences.
Show Example
import tensorflow as tf
import collections

# Represent the following array of sets as a sparse tensor: # a = np.array([[{1, 2}, {3}], [{4}, {5, 6}]]) a = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 2), ((0, 1, 0), 3), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ]) a = tf.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2, 2, 2])

# np.array([[{1, 3}, {2}], [{4, 5}, {5, 6, 7, 8}]]) b = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 3), ((0, 1, 0), 2), ((1, 0, 0), 4), ((1, 0, 1), 5), ((1, 1, 0), 5), ((1, 1, 1), 6), ((1, 1, 2), 7), ((1, 1, 3), 8), ]) b = tf.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])

# `set_difference` is applied to each aligned pair of sets. tf.sets.difference(a, b)

# The result will be equivalent to either of: # # np.array([[{2}, {3}], [{}, {}]]) # # collections.OrderedDict([ # ((0, 0, 0), 2), # ((0, 1, 0), 3), # ])

#### SparseTensordifference(IGraphNodeBase a, ValueTuple<IEnumerable<object>, object> b, bool aminusb, bool validate_indices)

Compute set difference of elements in last dimension of `a` and `b`.

All but the last dimension of `a` and `b` must match.

Example:
##### Parameters
IGraphNodeBase a
`Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices must be sorted in row-major order.
ValueTuple<IEnumerable<object>, object> b
`Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices must be sorted in row-major order.
bool aminusb
Whether to subtract `b` from `a`, vs vice versa.
bool validate_indices
Whether to validate the order and range of sparse indices in `a` and `b`.
##### Returns
SparseTensor
A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but the last dimension the same. Elements along the last dimension contain the differences.
Show Example
import tensorflow as tf
import collections

# Represent the following array of sets as a sparse tensor: # a = np.array([[{1, 2}, {3}], [{4}, {5, 6}]]) a = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 2), ((0, 1, 0), 3), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ]) a = tf.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2, 2, 2])

# np.array([[{1, 3}, {2}], [{4, 5}, {5, 6, 7, 8}]]) b = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 3), ((0, 1, 0), 2), ((1, 0, 0), 4), ((1, 0, 1), 5), ((1, 1, 0), 5), ((1, 1, 1), 6), ((1, 1, 2), 7), ((1, 1, 3), 8), ]) b = tf.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])

# `set_difference` is applied to each aligned pair of sets. tf.sets.difference(a, b)

# The result will be equivalent to either of: # # np.array([[{2}, {3}], [{}, {}]]) # # collections.OrderedDict([ # ((0, 0, 0), 2), # ((0, 1, 0), 3), # ])

#### SparseTensordifference(IGraphNodeBase a, IGraphNodeBase b, bool aminusb, bool validate_indices)

Compute set difference of elements in last dimension of `a` and `b`.

All but the last dimension of `a` and `b` must match.

Example:
##### Parameters
IGraphNodeBase a
`Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices must be sorted in row-major order.
IGraphNodeBase b
`Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices must be sorted in row-major order.
bool aminusb
Whether to subtract `b` from `a`, vs vice versa.
bool validate_indices
Whether to validate the order and range of sparse indices in `a` and `b`.
##### Returns
SparseTensor
A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but the last dimension the same. Elements along the last dimension contain the differences.
Show Example
import tensorflow as tf
import collections

# Represent the following array of sets as a sparse tensor: # a = np.array([[{1, 2}, {3}], [{4}, {5, 6}]]) a = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 2), ((0, 1, 0), 3), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ]) a = tf.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2, 2, 2])

# np.array([[{1, 3}, {2}], [{4, 5}, {5, 6, 7, 8}]]) b = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 3), ((0, 1, 0), 2), ((1, 0, 0), 4), ((1, 0, 1), 5), ((1, 1, 0), 5), ((1, 1, 1), 6), ((1, 1, 2), 7), ((1, 1, 3), 8), ]) b = tf.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])

# `set_difference` is applied to each aligned pair of sets. tf.sets.difference(a, b)

# The result will be equivalent to either of: # # np.array([[{2}, {3}], [{}, {}]]) # # collections.OrderedDict([ # ((0, 0, 0), 2), # ((0, 1, 0), 3), # ])

#### objectdifference_dyn(object a, object b, ImplicitContainer<T> aminusb, ImplicitContainer<T> validate_indices)

Compute set difference of elements in last dimension of `a` and `b`.

All but the last dimension of `a` and `b` must match.

Example:
##### Parameters
object a
`Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices must be sorted in row-major order.
object b
`Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices must be sorted in row-major order.
ImplicitContainer<T> aminusb
Whether to subtract `b` from `a`, vs vice versa.
ImplicitContainer<T> validate_indices
Whether to validate the order and range of sparse indices in `a` and `b`.
##### Returns
object
A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but the last dimension the same. Elements along the last dimension contain the differences.
Show Example
import tensorflow as tf
import collections

# Represent the following array of sets as a sparse tensor: # a = np.array([[{1, 2}, {3}], [{4}, {5, 6}]]) a = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 2), ((0, 1, 0), 3), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ]) a = tf.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2, 2, 2])

# np.array([[{1, 3}, {2}], [{4, 5}, {5, 6, 7, 8}]]) b = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 3), ((0, 1, 0), 2), ((1, 0, 0), 4), ((1, 0, 1), 5), ((1, 1, 0), 5), ((1, 1, 1), 6), ((1, 1, 2), 7), ((1, 1, 3), 8), ]) b = tf.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])

# `set_difference` is applied to each aligned pair of sets. tf.sets.difference(a, b)

# The result will be equivalent to either of: # # np.array([[{2}, {3}], [{}, {}]]) # # collections.OrderedDict([ # ((0, 0, 0), 2), # ((0, 1, 0), 3), # ])

#### SparseTensorintersection(IndexedSlices a, IGraphNodeBase b, bool validate_indices)

Compute set intersection of elements in last dimension of `a` and `b`.

All but the last dimension of `a` and `b` must match.

Example:
##### Parameters
IndexedSlices a
`Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices must be sorted in row-major order.
IGraphNodeBase b
`Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices must be sorted in row-major order.
bool validate_indices
Whether to validate the order and range of sparse indices in `a` and `b`.
##### Returns
SparseTensor
A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but the last dimension the same. Elements along the last dimension contain the intersections.
Show Example
import tensorflow as tf
import collections

# Represent the following array of sets as a sparse tensor: # a = np.array([[{1, 2}, {3}], [{4}, {5, 6}]]) a = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 2), ((0, 1, 0), 3), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ]) a = tf.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2,2,2])

# b = np.array([[{1}, {}], [{4}, {5, 6, 7, 8}]]) b = collections.OrderedDict([ ((0, 0, 0), 1), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ((1, 1, 2), 7), ((1, 1, 3), 8), ]) b = tf.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])

# tf.sets.intersection is applied to each aligned pair of sets. tf.sets.intersection(a, b)

# The result will be equivalent to either of: # # np.array([[{1}, {}], [{4}, {5, 6}]]) # # collections.OrderedDict([ # ((0, 0, 0), 1), # ((1, 0, 0), 4), # ((1, 1, 0), 5), # ((1, 1, 1), 6), # ])

#### SparseTensorintersection(ValueTuple<PythonClassContainer, PythonClassContainer> a, IEnumerable<object> b, bool validate_indices)

Compute set intersection of elements in last dimension of `a` and `b`.

All but the last dimension of `a` and `b` must match.

Example:
##### Parameters
ValueTuple<PythonClassContainer, PythonClassContainer> a
`Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices must be sorted in row-major order.
IEnumerable<object> b
`Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices must be sorted in row-major order.
bool validate_indices
Whether to validate the order and range of sparse indices in `a` and `b`.
##### Returns
SparseTensor
A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but the last dimension the same. Elements along the last dimension contain the intersections.
Show Example
import tensorflow as tf
import collections

# Represent the following array of sets as a sparse tensor: # a = np.array([[{1, 2}, {3}], [{4}, {5, 6}]]) a = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 2), ((0, 1, 0), 3), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ]) a = tf.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2,2,2])

# b = np.array([[{1}, {}], [{4}, {5, 6, 7, 8}]]) b = collections.OrderedDict([ ((0, 0, 0), 1), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ((1, 1, 2), 7), ((1, 1, 3), 8), ]) b = tf.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])

# tf.sets.intersection is applied to each aligned pair of sets. tf.sets.intersection(a, b)

# The result will be equivalent to either of: # # np.array([[{1}, {}], [{4}, {5, 6}]]) # # collections.OrderedDict([ # ((0, 0, 0), 1), # ((1, 0, 0), 4), # ((1, 1, 0), 5), # ((1, 1, 1), 6), # ])

#### SparseTensorintersection(ValueTuple<PythonClassContainer, PythonClassContainer> a, ValueTuple<PythonClassContainer, PythonClassContainer> b, bool validate_indices)

Compute set intersection of elements in last dimension of `a` and `b`.

All but the last dimension of `a` and `b` must match.

Example:
##### Parameters
ValueTuple<PythonClassContainer, PythonClassContainer> a
`Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices must be sorted in row-major order.
ValueTuple<PythonClassContainer, PythonClassContainer> b
`Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices must be sorted in row-major order.
bool validate_indices
Whether to validate the order and range of sparse indices in `a` and `b`.
##### Returns
SparseTensor
A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but the last dimension the same. Elements along the last dimension contain the intersections.
Show Example
import tensorflow as tf
import collections

# Represent the following array of sets as a sparse tensor: # a = np.array([[{1, 2}, {3}], [{4}, {5, 6}]]) a = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 2), ((0, 1, 0), 3), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ]) a = tf.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2,2,2])

# b = np.array([[{1}, {}], [{4}, {5, 6, 7, 8}]]) b = collections.OrderedDict([ ((0, 0, 0), 1), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ((1, 1, 2), 7), ((1, 1, 3), 8), ]) b = tf.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])

# tf.sets.intersection is applied to each aligned pair of sets. tf.sets.intersection(a, b)

# The result will be equivalent to either of: # # np.array([[{1}, {}], [{4}, {5, 6}]]) # # collections.OrderedDict([ # ((0, 0, 0), 1), # ((1, 0, 0), 4), # ((1, 1, 0), 5), # ((1, 1, 1), 6), # ])

#### SparseTensorintersection(ValueTuple<PythonClassContainer, PythonClassContainer> a, IndexedSlices b, bool validate_indices)

Compute set intersection of elements in last dimension of `a` and `b`.

All but the last dimension of `a` and `b` must match.

Example:
##### Parameters
ValueTuple<PythonClassContainer, PythonClassContainer> a
`Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices must be sorted in row-major order.
IndexedSlices b
`Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices must be sorted in row-major order.
bool validate_indices
Whether to validate the order and range of sparse indices in `a` and `b`.
##### Returns
SparseTensor
A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but the last dimension the same. Elements along the last dimension contain the intersections.
Show Example
import tensorflow as tf
import collections

# Represent the following array of sets as a sparse tensor: # a = np.array([[{1, 2}, {3}], [{4}, {5, 6}]]) a = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 2), ((0, 1, 0), 3), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ]) a = tf.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2,2,2])

# b = np.array([[{1}, {}], [{4}, {5, 6, 7, 8}]]) b = collections.OrderedDict([ ((0, 0, 0), 1), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ((1, 1, 2), 7), ((1, 1, 3), 8), ]) b = tf.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])

# tf.sets.intersection is applied to each aligned pair of sets. tf.sets.intersection(a, b)

# The result will be equivalent to either of: # # np.array([[{1}, {}], [{4}, {5, 6}]]) # # collections.OrderedDict([ # ((0, 0, 0), 1), # ((1, 0, 0), 4), # ((1, 1, 0), 5), # ((1, 1, 1), 6), # ])

#### SparseTensorintersection(ValueTuple<PythonClassContainer, PythonClassContainer> a, IGraphNodeBase b, bool validate_indices)

Compute set intersection of elements in last dimension of `a` and `b`.

All but the last dimension of `a` and `b` must match.

Example:
##### Parameters
ValueTuple<PythonClassContainer, PythonClassContainer> a
`Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices must be sorted in row-major order.
IGraphNodeBase b
`Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices must be sorted in row-major order.
bool validate_indices
Whether to validate the order and range of sparse indices in `a` and `b`.
##### Returns
SparseTensor
A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but the last dimension the same. Elements along the last dimension contain the intersections.
Show Example
import tensorflow as tf
import collections

# Represent the following array of sets as a sparse tensor: # a = np.array([[{1, 2}, {3}], [{4}, {5, 6}]]) a = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 2), ((0, 1, 0), 3), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ]) a = tf.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2,2,2])

# b = np.array([[{1}, {}], [{4}, {5, 6, 7, 8}]]) b = collections.OrderedDict([ ((0, 0, 0), 1), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ((1, 1, 2), 7), ((1, 1, 3), 8), ]) b = tf.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])

# tf.sets.intersection is applied to each aligned pair of sets. tf.sets.intersection(a, b)

# The result will be equivalent to either of: # # np.array([[{1}, {}], [{4}, {5, 6}]]) # # collections.OrderedDict([ # ((0, 0, 0), 1), # ((1, 0, 0), 4), # ((1, 1, 0), 5), # ((1, 1, 1), 6), # ])

#### SparseTensorintersection(ValueTuple<PythonClassContainer, PythonClassContainer> a, object b, bool validate_indices)

Compute set intersection of elements in last dimension of `a` and `b`.

All but the last dimension of `a` and `b` must match.

Example:
##### Parameters
ValueTuple<PythonClassContainer, PythonClassContainer> a
`Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices must be sorted in row-major order.
object b
`Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices must be sorted in row-major order.
bool validate_indices
Whether to validate the order and range of sparse indices in `a` and `b`.
##### Returns
SparseTensor
A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but the last dimension the same. Elements along the last dimension contain the intersections.
Show Example
import tensorflow as tf
import collections

# Represent the following array of sets as a sparse tensor: # a = np.array([[{1, 2}, {3}], [{4}, {5, 6}]]) a = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 2), ((0, 1, 0), 3), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ]) a = tf.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2,2,2])

# b = np.array([[{1}, {}], [{4}, {5, 6, 7, 8}]]) b = collections.OrderedDict([ ((0, 0, 0), 1), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ((1, 1, 2), 7), ((1, 1, 3), 8), ]) b = tf.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])

# tf.sets.intersection is applied to each aligned pair of sets. tf.sets.intersection(a, b)

# The result will be equivalent to either of: # # np.array([[{1}, {}], [{4}, {5, 6}]]) # # collections.OrderedDict([ # ((0, 0, 0), 1), # ((1, 0, 0), 4), # ((1, 1, 0), 5), # ((1, 1, 1), 6), # ])

#### SparseTensorintersection(IndexedSlices a, ndarray b, bool validate_indices)

Compute set intersection of elements in last dimension of `a` and `b`.

All but the last dimension of `a` and `b` must match.

Example:
##### Parameters
IndexedSlices a
`Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices must be sorted in row-major order.
ndarray b
`Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices must be sorted in row-major order.
bool validate_indices
Whether to validate the order and range of sparse indices in `a` and `b`.
##### Returns
SparseTensor
A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but the last dimension the same. Elements along the last dimension contain the intersections.
Show Example
import tensorflow as tf
import collections

# Represent the following array of sets as a sparse tensor: # a = np.array([[{1, 2}, {3}], [{4}, {5, 6}]]) a = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 2), ((0, 1, 0), 3), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ]) a = tf.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2,2,2])

# b = np.array([[{1}, {}], [{4}, {5, 6, 7, 8}]]) b = collections.OrderedDict([ ((0, 0, 0), 1), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ((1, 1, 2), 7), ((1, 1, 3), 8), ]) b = tf.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])

# tf.sets.intersection is applied to each aligned pair of sets. tf.sets.intersection(a, b)

# The result will be equivalent to either of: # # np.array([[{1}, {}], [{4}, {5, 6}]]) # # collections.OrderedDict([ # ((0, 0, 0), 1), # ((1, 0, 0), 4), # ((1, 1, 0), 5), # ((1, 1, 1), 6), # ])

#### SparseTensorintersection(IndexedSlices a, IEnumerable<object> b, bool validate_indices)

Compute set intersection of elements in last dimension of `a` and `b`.

All but the last dimension of `a` and `b` must match.

Example:
##### Parameters
IndexedSlices a
`Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices must be sorted in row-major order.
IEnumerable<object> b
`Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices must be sorted in row-major order.
bool validate_indices
Whether to validate the order and range of sparse indices in `a` and `b`.
##### Returns
SparseTensor
A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but the last dimension the same. Elements along the last dimension contain the intersections.
Show Example
import tensorflow as tf
import collections

# Represent the following array of sets as a sparse tensor: # a = np.array([[{1, 2}, {3}], [{4}, {5, 6}]]) a = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 2), ((0, 1, 0), 3), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ]) a = tf.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2,2,2])

# b = np.array([[{1}, {}], [{4}, {5, 6, 7, 8}]]) b = collections.OrderedDict([ ((0, 0, 0), 1), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ((1, 1, 2), 7), ((1, 1, 3), 8), ]) b = tf.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])

# tf.sets.intersection is applied to each aligned pair of sets. tf.sets.intersection(a, b)

# The result will be equivalent to either of: # # np.array([[{1}, {}], [{4}, {5, 6}]]) # # collections.OrderedDict([ # ((0, 0, 0), 1), # ((1, 0, 0), 4), # ((1, 1, 0), 5), # ((1, 1, 1), 6), # ])

#### SparseTensorintersection(IndexedSlices a, IndexedSlices b, bool validate_indices)

Compute set intersection of elements in last dimension of `a` and `b`.

All but the last dimension of `a` and `b` must match.

Example:
##### Parameters
IndexedSlices a
`Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices must be sorted in row-major order.
IndexedSlices b
`Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices must be sorted in row-major order.
bool validate_indices
Whether to validate the order and range of sparse indices in `a` and `b`.
##### Returns
SparseTensor
A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but the last dimension the same. Elements along the last dimension contain the intersections.
Show Example
import tensorflow as tf
import collections

# Represent the following array of sets as a sparse tensor: # a = np.array([[{1, 2}, {3}], [{4}, {5, 6}]]) a = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 2), ((0, 1, 0), 3), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ]) a = tf.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2,2,2])

# b = np.array([[{1}, {}], [{4}, {5, 6, 7, 8}]]) b = collections.OrderedDict([ ((0, 0, 0), 1), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ((1, 1, 2), 7), ((1, 1, 3), 8), ]) b = tf.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])

# tf.sets.intersection is applied to each aligned pair of sets. tf.sets.intersection(a, b)

# The result will be equivalent to either of: # # np.array([[{1}, {}], [{4}, {5, 6}]]) # # collections.OrderedDict([ # ((0, 0, 0), 1), # ((1, 0, 0), 4), # ((1, 1, 0), 5), # ((1, 1, 1), 6), # ])

#### SparseTensorintersection(IndexedSlices a, ValueTuple<PythonClassContainer, PythonClassContainer> b, bool validate_indices)

Compute set intersection of elements in last dimension of `a` and `b`.

All but the last dimension of `a` and `b` must match.

Example:
##### Parameters
IndexedSlices a
`Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices must be sorted in row-major order.
ValueTuple<PythonClassContainer, PythonClassContainer> b
`Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices must be sorted in row-major order.
bool validate_indices
Whether to validate the order and range of sparse indices in `a` and `b`.
##### Returns
SparseTensor
A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but the last dimension the same. Elements along the last dimension contain the intersections.
Show Example
import tensorflow as tf
import collections

# Represent the following array of sets as a sparse tensor: # a = np.array([[{1, 2}, {3}], [{4}, {5, 6}]]) a = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 2), ((0, 1, 0), 3), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ]) a = tf.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2,2,2])

# b = np.array([[{1}, {}], [{4}, {5, 6, 7, 8}]]) b = collections.OrderedDict([ ((0, 0, 0), 1), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ((1, 1, 2), 7), ((1, 1, 3), 8), ]) b = tf.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])

# tf.sets.intersection is applied to each aligned pair of sets. tf.sets.intersection(a, b)

# The result will be equivalent to either of: # # np.array([[{1}, {}], [{4}, {5, 6}]]) # # collections.OrderedDict([ # ((0, 0, 0), 1), # ((1, 0, 0), 4), # ((1, 1, 0), 5), # ((1, 1, 1), 6), # ])

#### SparseTensorintersection(IGraphNodeBase a, object b, bool validate_indices)

Compute set intersection of elements in last dimension of `a` and `b`.

All but the last dimension of `a` and `b` must match.

Example:
##### Parameters
IGraphNodeBase a
`Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices must be sorted in row-major order.
object b
`Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices must be sorted in row-major order.
bool validate_indices
Whether to validate the order and range of sparse indices in `a` and `b`.
##### Returns
SparseTensor
A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but the last dimension the same. Elements along the last dimension contain the intersections.
Show Example
import tensorflow as tf
import collections

# Represent the following array of sets as a sparse tensor: # a = np.array([[{1, 2}, {3}], [{4}, {5, 6}]]) a = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 2), ((0, 1, 0), 3), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ]) a = tf.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2,2,2])

# b = np.array([[{1}, {}], [{4}, {5, 6, 7, 8}]]) b = collections.OrderedDict([ ((0, 0, 0), 1), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ((1, 1, 2), 7), ((1, 1, 3), 8), ]) b = tf.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])

# tf.sets.intersection is applied to each aligned pair of sets. tf.sets.intersection(a, b)

# The result will be equivalent to either of: # # np.array([[{1}, {}], [{4}, {5, 6}]]) # # collections.OrderedDict([ # ((0, 0, 0), 1), # ((1, 0, 0), 4), # ((1, 1, 0), 5), # ((1, 1, 1), 6), # ])

#### SparseTensorintersection(IGraphNodeBase a, IGraphNodeBase b, bool validate_indices)

Compute set intersection of elements in last dimension of `a` and `b`.

All but the last dimension of `a` and `b` must match.

Example:
##### Parameters
IGraphNodeBase a
`Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices must be sorted in row-major order.
IGraphNodeBase b
`Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices must be sorted in row-major order.
bool validate_indices
Whether to validate the order and range of sparse indices in `a` and `b`.
##### Returns
SparseTensor
A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but the last dimension the same. Elements along the last dimension contain the intersections.
Show Example
import tensorflow as tf
import collections

# Represent the following array of sets as a sparse tensor: # a = np.array([[{1, 2}, {3}], [{4}, {5, 6}]]) a = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 2), ((0, 1, 0), 3), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ]) a = tf.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2,2,2])

# b = np.array([[{1}, {}], [{4}, {5, 6, 7, 8}]]) b = collections.OrderedDict([ ((0, 0, 0), 1), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ((1, 1, 2), 7), ((1, 1, 3), 8), ]) b = tf.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])

# tf.sets.intersection is applied to each aligned pair of sets. tf.sets.intersection(a, b)

# The result will be equivalent to either of: # # np.array([[{1}, {}], [{4}, {5, 6}]]) # # collections.OrderedDict([ # ((0, 0, 0), 1), # ((1, 0, 0), 4), # ((1, 1, 0), 5), # ((1, 1, 1), 6), # ])

#### SparseTensorintersection(IGraphNodeBase a, IndexedSlices b, bool validate_indices)

Compute set intersection of elements in last dimension of `a` and `b`.

All but the last dimension of `a` and `b` must match.

Example:
##### Parameters
IGraphNodeBase a
`Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices must be sorted in row-major order.
IndexedSlices b
`Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices must be sorted in row-major order.
bool validate_indices
Whether to validate the order and range of sparse indices in `a` and `b`.
##### Returns
SparseTensor
A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but the last dimension the same. Elements along the last dimension contain the intersections.
Show Example
import tensorflow as tf
import collections

# Represent the following array of sets as a sparse tensor: # a = np.array([[{1, 2}, {3}], [{4}, {5, 6}]]) a = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 2), ((0, 1, 0), 3), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ]) a = tf.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2,2,2])

# b = np.array([[{1}, {}], [{4}, {5, 6, 7, 8}]]) b = collections.OrderedDict([ ((0, 0, 0), 1), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ((1, 1, 2), 7), ((1, 1, 3), 8), ]) b = tf.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])

# tf.sets.intersection is applied to each aligned pair of sets. tf.sets.intersection(a, b)

# The result will be equivalent to either of: # # np.array([[{1}, {}], [{4}, {5, 6}]]) # # collections.OrderedDict([ # ((0, 0, 0), 1), # ((1, 0, 0), 4), # ((1, 1, 0), 5), # ((1, 1, 1), 6), # ])

#### SparseTensorintersection(IGraphNodeBase a, ValueTuple<PythonClassContainer, PythonClassContainer> b, bool validate_indices)

Compute set intersection of elements in last dimension of `a` and `b`.

All but the last dimension of `a` and `b` must match.

Example:
##### Parameters
IGraphNodeBase a
`Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices must be sorted in row-major order.
ValueTuple<PythonClassContainer, PythonClassContainer> b
`Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices must be sorted in row-major order.
bool validate_indices
Whether to validate the order and range of sparse indices in `a` and `b`.
##### Returns
SparseTensor
A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but the last dimension the same. Elements along the last dimension contain the intersections.
Show Example
import tensorflow as tf
import collections

# Represent the following array of sets as a sparse tensor: # a = np.array([[{1, 2}, {3}], [{4}, {5, 6}]]) a = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 2), ((0, 1, 0), 3), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ]) a = tf.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2,2,2])

# b = np.array([[{1}, {}], [{4}, {5, 6, 7, 8}]]) b = collections.OrderedDict([ ((0, 0, 0), 1), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ((1, 1, 2), 7), ((1, 1, 3), 8), ]) b = tf.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])

# tf.sets.intersection is applied to each aligned pair of sets. tf.sets.intersection(a, b)

# The result will be equivalent to either of: # # np.array([[{1}, {}], [{4}, {5, 6}]]) # # collections.OrderedDict([ # ((0, 0, 0), 1), # ((1, 0, 0), 4), # ((1, 1, 0), 5), # ((1, 1, 1), 6), # ])

#### SparseTensorintersection(IGraphNodeBase a, ndarray b, bool validate_indices)

Compute set intersection of elements in last dimension of `a` and `b`.

All but the last dimension of `a` and `b` must match.

Example:
##### Parameters
IGraphNodeBase a
`Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices must be sorted in row-major order.
ndarray b
`Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices must be sorted in row-major order.
bool validate_indices
Whether to validate the order and range of sparse indices in `a` and `b`.
##### Returns
SparseTensor
A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but the last dimension the same. Elements along the last dimension contain the intersections.
Show Example
import tensorflow as tf
import collections

# Represent the following array of sets as a sparse tensor: # a = np.array([[{1, 2}, {3}], [{4}, {5, 6}]]) a = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 2), ((0, 1, 0), 3), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ]) a = tf.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2,2,2])

# b = np.array([[{1}, {}], [{4}, {5, 6, 7, 8}]]) b = collections.OrderedDict([ ((0, 0, 0), 1), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ((1, 1, 2), 7), ((1, 1, 3), 8), ]) b = tf.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])

# tf.sets.intersection is applied to each aligned pair of sets. tf.sets.intersection(a, b)

# The result will be equivalent to either of: # # np.array([[{1}, {}], [{4}, {5, 6}]]) # # collections.OrderedDict([ # ((0, 0, 0), 1), # ((1, 0, 0), 4), # ((1, 1, 0), 5), # ((1, 1, 1), 6), # ])

#### SparseTensorintersection(IndexedSlices a, object b, bool validate_indices)

Compute set intersection of elements in last dimension of `a` and `b`.

All but the last dimension of `a` and `b` must match.

Example:
##### Parameters
IndexedSlices a
`Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices must be sorted in row-major order.
object b
`Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices must be sorted in row-major order.
bool validate_indices
Whether to validate the order and range of sparse indices in `a` and `b`.
##### Returns
SparseTensor
A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but the last dimension the same. Elements along the last dimension contain the intersections.
Show Example
import tensorflow as tf
import collections

# Represent the following array of sets as a sparse tensor: # a = np.array([[{1, 2}, {3}], [{4}, {5, 6}]]) a = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 2), ((0, 1, 0), 3), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ]) a = tf.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2,2,2])

# b = np.array([[{1}, {}], [{4}, {5, 6, 7, 8}]]) b = collections.OrderedDict([ ((0, 0, 0), 1), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ((1, 1, 2), 7), ((1, 1, 3), 8), ]) b = tf.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])

# tf.sets.intersection is applied to each aligned pair of sets. tf.sets.intersection(a, b)

# The result will be equivalent to either of: # # np.array([[{1}, {}], [{4}, {5, 6}]]) # # collections.OrderedDict([ # ((0, 0, 0), 1), # ((1, 0, 0), 4), # ((1, 1, 0), 5), # ((1, 1, 1), 6), # ])

#### SparseTensorintersection(ValueTuple<PythonClassContainer, PythonClassContainer> a, ndarray b, bool validate_indices)

Compute set intersection of elements in last dimension of `a` and `b`.

All but the last dimension of `a` and `b` must match.

Example:
##### Parameters
ValueTuple<PythonClassContainer, PythonClassContainer> a
`Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices must be sorted in row-major order.
ndarray b
`Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices must be sorted in row-major order.
bool validate_indices
Whether to validate the order and range of sparse indices in `a` and `b`.
##### Returns
SparseTensor
A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but the last dimension the same. Elements along the last dimension contain the intersections.
Show Example
import tensorflow as tf
import collections

# Represent the following array of sets as a sparse tensor: # a = np.array([[{1, 2}, {3}], [{4}, {5, 6}]]) a = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 2), ((0, 1, 0), 3), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ]) a = tf.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2,2,2])

# b = np.array([[{1}, {}], [{4}, {5, 6, 7, 8}]]) b = collections.OrderedDict([ ((0, 0, 0), 1), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ((1, 1, 2), 7), ((1, 1, 3), 8), ]) b = tf.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])

# tf.sets.intersection is applied to each aligned pair of sets. tf.sets.intersection(a, b)

# The result will be equivalent to either of: # # np.array([[{1}, {}], [{4}, {5, 6}]]) # # collections.OrderedDict([ # ((0, 0, 0), 1), # ((1, 0, 0), 4), # ((1, 1, 0), 5), # ((1, 1, 1), 6), # ])

#### SparseTensorintersection(IGraphNodeBase a, IEnumerable<object> b, bool validate_indices)

Compute set intersection of elements in last dimension of `a` and `b`.

All but the last dimension of `a` and `b` must match.

Example:
##### Parameters
IGraphNodeBase a
`Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices must be sorted in row-major order.
IEnumerable<object> b
`Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices must be sorted in row-major order.
bool validate_indices
Whether to validate the order and range of sparse indices in `a` and `b`.
##### Returns
SparseTensor
A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but the last dimension the same. Elements along the last dimension contain the intersections.
Show Example
import tensorflow as tf
import collections

# Represent the following array of sets as a sparse tensor: # a = np.array([[{1, 2}, {3}], [{4}, {5, 6}]]) a = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 2), ((0, 1, 0), 3), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ]) a = tf.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2,2,2])

# b = np.array([[{1}, {}], [{4}, {5, 6, 7, 8}]]) b = collections.OrderedDict([ ((0, 0, 0), 1), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ((1, 1, 2), 7), ((1, 1, 3), 8), ]) b = tf.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])

# tf.sets.intersection is applied to each aligned pair of sets. tf.sets.intersection(a, b)

# The result will be equivalent to either of: # # np.array([[{1}, {}], [{4}, {5, 6}]]) # # collections.OrderedDict([ # ((0, 0, 0), 1), # ((1, 0, 0), 4), # ((1, 1, 0), 5), # ((1, 1, 1), 6), # ])

#### objectintersection_dyn(object a, object b, ImplicitContainer<T> validate_indices)

Compute set intersection of elements in last dimension of `a` and `b`.

All but the last dimension of `a` and `b` must match.

Example:
##### Parameters
object a
`Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices must be sorted in row-major order.
object b
`Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices must be sorted in row-major order.
ImplicitContainer<T> validate_indices
Whether to validate the order and range of sparse indices in `a` and `b`.
##### Returns
object
A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but the last dimension the same. Elements along the last dimension contain the intersections.
Show Example
import tensorflow as tf
import collections

# Represent the following array of sets as a sparse tensor: # a = np.array([[{1, 2}, {3}], [{4}, {5, 6}]]) a = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 2), ((0, 1, 0), 3), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ]) a = tf.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2,2,2])

# b = np.array([[{1}, {}], [{4}, {5, 6, 7, 8}]]) b = collections.OrderedDict([ ((0, 0, 0), 1), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ((1, 1, 2), 7), ((1, 1, 3), 8), ]) b = tf.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])

# tf.sets.intersection is applied to each aligned pair of sets. tf.sets.intersection(a, b)

# The result will be equivalent to either of: # # np.array([[{1}, {}], [{4}, {5, 6}]]) # # collections.OrderedDict([ # ((0, 0, 0), 1), # ((1, 0, 0), 4), # ((1, 1, 0), 5), # ((1, 1, 1), 6), # ])

#### Tensorsize(SparseTensor a, bool validate_indices)

Compute number of unique elements along last dimension of `a`.
##### Parameters
SparseTensor a
`SparseTensor`, with indices sorted in row-major order.
bool validate_indices
Whether to validate the order and range of sparse indices in `a`.
##### Returns
Tensor
`int32` `Tensor` of set sizes. For `a` ranked `n`, this is a `Tensor` with rank `n-1`, and the same 1st `n-1` dimensions as `a`. Each value is the number of unique elements in the corresponding `[0...n-1]` dimension of `a`.

#### objectsize_dyn(object a, ImplicitContainer<T> validate_indices)

Compute number of unique elements along last dimension of `a`.
##### Parameters
object a
`SparseTensor`, with indices sorted in row-major order.
ImplicitContainer<T> validate_indices
Whether to validate the order and range of sparse indices in `a`.
##### Returns
object
`int32` `Tensor` of set sizes. For `a` ranked `n`, this is a `Tensor` with rank `n-1`, and the same 1st `n-1` dimensions as `a`. Each value is the number of unique elements in the corresponding `[0...n-1]` dimension of `a`.

#### SparseTensorunion(IGraphNodeBase a, IGraphNodeBase b, bool validate_indices)

Compute set union of elements in last dimension of `a` and `b`.

All but the last dimension of `a` and `b` must match.

Example:
##### Parameters
IGraphNodeBase a
`Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices must be sorted in row-major order.
IGraphNodeBase b
`Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices must be sorted in row-major order.
bool validate_indices
Whether to validate the order and range of sparse indices in `a` and `b`.
##### Returns
SparseTensor
A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but the last dimension the same. Elements along the last dimension contain the unions.
Show Example
import tensorflow as tf
import collections

# [[{1, 2}, {3}], [{4}, {5, 6}]] a = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 2), ((0, 1, 0), 3), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ]) a = tf.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2, 2, 2])

# [[{1, 3}, {2}], [{4, 5}, {5, 6, 7, 8}]] b = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 3), ((0, 1, 0), 2), ((1, 0, 0), 4), ((1, 0, 1), 5), ((1, 1, 0), 5), ((1, 1, 1), 6), ((1, 1, 2), 7), ((1, 1, 3), 8), ]) b = tf.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])

# `set_union` is applied to each aligned pair of sets. tf.sets.union(a, b)

# The result will be a equivalent to either of: # # np.array([[{1, 2, 3}, {2, 3}], [{4, 5}, {5, 6, 7, 8}]]) # # collections.OrderedDict([ # ((0, 0, 0), 1), # ((0, 0, 1), 2), # ((0, 0, 2), 3), # ((0, 1, 0), 2), # ((0, 1, 1), 3), # ((1, 0, 0), 4), # ((1, 0, 1), 5), # ((1, 1, 0), 5), # ((1, 1, 1), 6), # ((1, 1, 2), 7), # ((1, 1, 3), 8), # ])

#### objectunion_dyn(object a, object b, ImplicitContainer<T> validate_indices)

Compute set union of elements in last dimension of `a` and `b`.

All but the last dimension of `a` and `b` must match.

Example:
##### Parameters
object a
`Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices must be sorted in row-major order.
object b
`Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices must be sorted in row-major order.
ImplicitContainer<T> validate_indices
Whether to validate the order and range of sparse indices in `a` and `b`.
##### Returns
object
A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but the last dimension the same. Elements along the last dimension contain the unions.
Show Example
import tensorflow as tf
import collections

# [[{1, 2}, {3}], [{4}, {5, 6}]] a = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 2), ((0, 1, 0), 3), ((1, 0, 0), 4), ((1, 1, 0), 5), ((1, 1, 1), 6), ]) a = tf.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2, 2, 2])

# [[{1, 3}, {2}], [{4, 5}, {5, 6, 7, 8}]] b = collections.OrderedDict([ ((0, 0, 0), 1), ((0, 0, 1), 3), ((0, 1, 0), 2), ((1, 0, 0), 4), ((1, 0, 1), 5), ((1, 1, 0), 5), ((1, 1, 1), 6), ((1, 1, 2), 7), ((1, 1, 3), 8), ]) b = tf.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])

# `set_union` is applied to each aligned pair of sets. tf.sets.union(a, b)

# The result will be a equivalent to either of: # # np.array([[{1, 2, 3}, {2, 3}], [{4, 5}, {5, 6, 7, 8}]]) # # collections.OrderedDict([ # ((0, 0, 0), 1), # ((0, 0, 1), 2), # ((0, 0, 2), 3), # ((0, 1, 0), 2), # ((0, 1, 1), 3), # ((1, 0, 0), 4), # ((1, 0, 1), 5), # ((1, 1, 0), 5), # ((1, 1, 1), 6), # ((1, 1, 2), 7), # ((1, 1, 3), 8), # ])