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# tf.raw_ops.Svd

Computes the singular value decompositions of one or more matrices.

Computes the SVD of each inner matrix in `input` such that `input[..., :, :] = u[..., :, :] * diag(s[..., :, :]) * transpose(v[..., :, :])`

``````# a is a tensor containing a batch of matrices.
# s is a tensor of singular values for each matrix.
# u is the tensor containing the left singular vectors for each matrix.
# v is the tensor containing the right singular vectors for each matrix.
s, u, v = svd(a)
s, _, _ = svd(a, compute_uv=False)
``````

`input` A `Tensor`. Must be one of the following types: `float64`, `float32`, `half`, `complex64`, `complex128`. A tensor of shape `[..., M, N]` whose inner-most 2 dimensions form matrices of size `[M, N]`. Let `P` be the minimum of `M` and `N`.
`compute_uv` An optional `bool`. Defaults to `True`. If true, left and right singular vectors will be computed and returned in `u` and `v`, respectively. If false, `u` and `v` are not set and should never referenced.
`full_matrices` An optional `bool`. Defaults to `False`. If true, compute full-sized `u` and `v`. If false (the default), compute only the leading `P` singular vectors. Ignored if `compute_uv` is `False`.
`name` A name for the operation (optional).

A tuple of `Tensor` objects (s, u, v).
`s` A `Tensor`. Has the same type as `input`.
`u` A `Tensor`. Has the same type as `input`.
`v` A `Tensor`. Has the same type as `input`.

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