# SegmentMinV2

public final class SegmentMinV2

Computes the minimum along segments of a tensor.

Read [the section on segmentation](https://tensorflow.org/api_docs/python/tf/math#Segmentation) for an explanation of segments.

Computes a tensor such that \$$output_i = \min_j(data_j)\$$ where min is over j such that segment_ids[j] == i.

If the minimum is empty for a given segment ID i, it outputs the largest possible value for the specific numeric type, output[i] = numeric_limits::max().

Note: That this op is currently only supported with jit_compile=True.

Caution: On CPU, values in segment_ids are always validated to be sorted, and an error is thrown for indices that are not increasing. On GPU, this does not throw an error for unsorted indices. On GPU, out-of-order indices result in safe but unspecified behavior, which may include treating out-of-order indices as the same as a smaller following index.

The only difference with SegmentMin is the additional input num_segments. This helps in evaluating the output shape in compile time. num_segments should be consistent with segment_ids. e.g. Max(segment_ids) should be equal to num_segments - 1 for a 1-d segment_ids With inconsistent num_segments, the op still runs. only difference is, the output takes the size of num_segments irrespective of size of segment_ids and data. for num_segments less than expected output size, the last elements are ignored for num_segments more than the expected output size, last elements are assigned the largest possible value for the specific numeric type.

For example:

>>> @tf.function(jit_compile=True) ... def test(c): ... return tf.raw_ops.SegmentMinV2(data=c, segment_ids=tf.constant([0, 0, 1]), num_segments=2) >>> c = tf.constant([[1,2,3,4], [4, 3, 2, 1], [5,6,7,8]]) >>> test(c).numpy() array([[1, 2, 2, 1], [5, 6, 7, 8]], dtype=int32)

### Public Methods

 Output asOutput() Returns the symbolic handle of a tensor. static SegmentMinV2 create(Scope scope, Operand data, Operand segmentIds, Operand numSegments) Factory method to create a class wrapping a new SegmentMinV2 operation. Output output() Has same shape as data, except for the first segment_ids.rank dimensions, which are replaced with a single dimensionw which has size num_segments.

## Public Methods

#### public Output<T> asOutput()

Returns the symbolic handle of a tensor.

Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is used to obtain a symbolic handle that represents the computation of the input.

#### public static SegmentMinV2<T> create(Scope scope, Operand<T> data, Operand<U> segmentIds, Operand<V> numSegments)

Factory method to create a class wrapping a new SegmentMinV2 operation.

##### Parameters
scope current scope A 1-D tensor whose size is equal to the size of data's first dimension. Values should be sorted and can be repeated. The values must be less than num_segments. Caution: The values are always validated to be sorted on CPU, never validated on GPU.
##### Returns
• a new instance of SegmentMinV2

#### public Output<T> output()

Has same shape as data, except for the first segment_ids.rank dimensions, which are replaced with a single dimensionw which has size num_segments.

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