# tf.raw_ops.MatrixDiagV2

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Returns a batched diagonal tensor with given 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.MatrixDiagV2`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/MatrixDiagV2)

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    tf.raw_ops.MatrixDiagV2(
        diagonal, k, num_rows, num_cols, padding_value, name=None
    )

Returns a tensor with the contents in `diagonal` as `k[0]`-th to `k[1]`-th
diagonals of a matrix, with everything else padded with `padding`. `num_rows`
and `num_cols` specify the dimension of the innermost matrix of the output. If
both are not specified, the op assumes the innermost matrix is square and infers
its size from `k` and the innermost dimension of `diagonal`. If only one of them
is specified, the op assumes the unspecified value is the smallest possible
based on other criteria.

Let `diagonal` have `r` dimensions `[I, J, ..., L, M, N]`. The output tensor has
rank `r+1` with shape `[I, J, ..., L, M, num_rows, num_cols]` when only one
diagonal is given (`k` is an integer or `k[0] == k[1]`). Otherwise, it has rank
`r` with shape `[I, J, ..., L, num_rows, num_cols]`.

The second innermost dimension of `diagonal` has double meaning.
When `k` is scalar or `k[0] == k[1]`, `M` is part of the batch size
\[I, J, ..., M\], and the output tensor is:

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

Otherwise, `M` is treated as the number of diagonals for the matrix in the
same batch (`M = k[1]-k[0]+1`), and the output tensor is:

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

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

#### For example:

    # The main diagonal.
    diagonal = np.array([[1, 2, 3, 4],            # Input shape: (2, 4)
                         [5, 6, 7, 8]])
    tf.matrix_diag(diagonal) ==> [[[1, 0, 0, 0],  # Output shape: (2, 4, 4)
                                   [0, 2, 0, 0],
                                   [0, 0, 3, 0],
                                   [0, 0, 0, 4]],
                                  [[5, 0, 0, 0],
                                   [0, 6, 0, 0],
                                   [0, 0, 7, 0],
                                   [0, 0, 0, 8]]]

    # A superdiagonal (per batch).
    diagonal = np.array([[1, 2, 3],  # Input shape: (2, 3)
                         [4, 5, 6]])
    tf.matrix_diag(diagonal, k = 1)
      ==> [[[0, 1, 0, 0],  # Output shape: (2, 4, 4)
            [0, 0, 2, 0],
            [0, 0, 0, 3],
            [0, 0, 0, 0]],
           [[0, 4, 0, 0],
            [0, 0, 5, 0],
            [0, 0, 0, 6],
            [0, 0, 0, 0]]]

    # A band of diagonals.
    diagonals = np.array([[[1, 2, 3],  # Input shape: (2, 2, 3)
                           [4, 5, 0]],
                          [[6, 7, 9],
                           [9, 1, 0]]])
    tf.matrix_diag(diagonals, k = (-1, 0))
      ==> [[[1, 0, 0],  # Output shape: (2, 3, 3)
            [4, 2, 0],
            [0, 5, 3]],
           [[6, 0, 0],
            [9, 7, 0],
            [0, 1, 9]]]

    # Rectangular matrix.
    diagonal = np.array([1, 2])  # Input shape: (2)
    tf.matrix_diag(diagonal, k = -1, num_rows = 3, num_cols = 4)
      ==> [[0, 0, 0, 0],  # Output shape: (3, 4)
           [1, 0, 0, 0],
           [0, 2, 0, 0]]

    # Rectangular matrix with inferred num_cols and padding_value = 9.
    tf.matrix_diag(diagonal, k = -1, num_rows = 3, padding_value = 9)
      ==> [[9, 9],  # Output shape: (3, 2)
           [1, 9],
           [9, 2]]

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| ## Args ||
|---|---|
| `diagonal` | A `Tensor`. Rank `r`, where `r >= 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]`. |
| `num_rows` | A `Tensor` of type `int32`. The number of rows of the output matrix. If it is not provided, the op assumes the output matrix is a square matrix and infers the matrix size from k and the innermost dimension of `diagonal`. |
| `num_cols` | A `Tensor` of type `int32`. The number of columns of the output matrix. If it is not provided, the op assumes the output matrix is a square matrix and infers the matrix size from k and the innermost dimension of `diagonal`. |
| `padding_value` | A `Tensor`. Must have the same type as `diagonal`. The number to fill the area outside the specified diagonal band with. Default is 0. |
| `name` | A name for the operation (optional). |

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

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