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Numpy equivalent is `tensor[mask]`.

In general, `0 < dim(mask) = K <= dim(tensor)`, and `mask`'s shape must match the first K dimensions of `tensor`'s shape. We then have: `boolean_mask(tensor, mask)[i, j1,...,jd] = tensor[i1,...,iK,j1,...,jd]` where `(i1,...,iK)` is the ith `True` entry of `mask` (row-major order). The `axis` could be used with `mask` to indicate the axis to mask from. In that case, `axis + dim(mask) <= dim(tensor)` and `mask`'s shape must match the first `axis + dim(mask)` dimensions of `tensor`'s shape.

See also: `tf.ragged.boolean_mask`, which can be applied to both dense and ragged tensors, and can be used if you need to preserve the masked dimensions of `tensor` (rather than flattening them, as `tf.boolean_mask` does).

Examples:

``````# 1-D example
tensor = [0, 1, 2, 3]
mask = np.array([True, False, True, False])

# 2-D example
tensor = [[1, 2], [3, 4], [5, 6]]
`tensor` N-D Tensor.
`mask` K-D boolean Tensor, K <= N and K must be known statically.
`name` A name for this operation (optional).
`axis` A 0-D int Tensor representing the axis in `tensor` to mask from. By default, axis is 0 which will mask from the first dimension. Otherwise K + axis <= N.
(N-K+1)-dimensional tensor populated by entries in `tensor` corresponding to `True` values in `mask`.
`ValueError` If shapes do not conform.