# tf.raw_ops.MatrixSetDiagV2

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Returns a batched matrix tensor with new batched diagonal values.

#### View aliases


**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

[`tf.compat.v1.raw_ops.MatrixSetDiagV2`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/MatrixSetDiagV2)

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    tf.raw_ops.MatrixSetDiagV2(
        input, diagonal, k, name=None
    )

Given `input` and `diagonal`, this operation returns a tensor with the
same shape and values as `input`, except for the specified diagonals of the
innermost matrices. These will be overwritten by the values in `diagonal`.

`input` has `r+1` dimensions `[I, J, ..., L, M, N]`. When `k` is scalar or
`k[0] == k[1]`, `diagonal` has `r` dimensions `[I, J, ..., L, max_diag_len]`.
Otherwise, it has `r+1` dimensions `[I, J, ..., L, num_diags, max_diag_len]`.
`num_diags` is the number of diagonals, `num_diags = k[1] - k[0] + 1`.
`max_diag_len` is the longest diagonal in the range `[k[0], k[1]]`,
`max_diag_len = min(M + min(k[1], 0), N + min(-k[0], 0))`

The output is a tensor of rank `k+1` with dimensions `[I, J, ..., L, M, N]`.
If `k` is scalar or `k[0] == k[1]`:

    output[i, j, ..., l, m, n]
      = diagonal[i, j, ..., l, n-max(k[1], 0)] ; if n - m == k[1]
        input[i, j, ..., l, m, n]              ; otherwise

Otherwise,

    output[i, j, ..., l, m, n]
      = diagonal[i, j, ..., l, diag_index, index_in_diag] ; if k[0] <= d <= k[1]
        input[i, j, ..., l, m, n]                         ; otherwise

where `d = n - m`, `diag_index = k[1] - d`, and `index_in_diag = n - max(d, 0)`.

#### For example:

    # The main diagonal.
    input = np.array([[[7, 7, 7, 7],              # Input shape: (2, 3, 4)
                       [7, 7, 7, 7],
                       [7, 7, 7, 7]],
                      [[7, 7, 7, 7],
                       [7, 7, 7, 7],
                       [7, 7, 7, 7]]])
    diagonal = np.array([[1, 2, 3],               # Diagonal shape: (2, 3)
                         [4, 5, 6]])
    tf.matrix_set_diag(diagonal) ==> [[[1, 7, 7, 7],  # Output shape: (2, 3, 4)
                                       [7, 2, 7, 7],
                                       [7, 7, 3, 7]],
                                      [[4, 7, 7, 7],
                                       [7, 5, 7, 7],
                                       [7, 7, 6, 7]]]

    # A superdiagonal (per batch).
    tf.matrix_set_diag(diagonal, k = 1)
      ==> [[[7, 1, 7, 7],  # Output shape: (2, 3, 4)
            [7, 7, 2, 7],
            [7, 7, 7, 3]],
           [[7, 4, 7, 7],
            [7, 7, 5, 7],
            [7, 7, 7, 6]]]

    # A band of diagonals.
    diagonals = np.array([[[1, 2, 3],  # Diagonal shape: (2, 2, 3)
                           [4, 5, 0]],
                          [[6, 1, 2],
                           [3, 4, 0]]])
    tf.matrix_set_diag(diagonals, k = (-1, 0))
      ==> [[[1, 7, 7, 7],  # Output shape: (2, 3, 4)
            [4, 2, 7, 7],
            [0, 5, 3, 7]],
           [[6, 7, 7, 7],
            [3, 1, 7, 7],
            [7, 4, 2, 7]]]

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| ## Args ||
|---|---|
| `input` | A `Tensor`. Rank `r+1`, where `r >= 1`. |
| `diagonal` | A `Tensor`. Must have the same type as `input`. Rank `r` when `k` is an integer or `k[0] == k[1]`. Otherwise, it has rank `r+1`. `k >= 1`. |
| `k` | A `Tensor` of type `int32`. Diagonal offset(s). Positive value means superdiagonal, 0 refers to the main diagonal, and negative value means subdiagonals. `k` can be a single integer (for a single diagonal) or a pair of integers specifying the low and high ends of a matrix band. `k[0]` must not be larger than `k[1]`. |
| `name` | A name for the operation (optional). |

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| ## Returns ||
|---|---|
| A `Tensor`. Has the same type as `input`. ||

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