# tf.raw_ops.MatrixDiagPartV2

<br />

Returns the batched diagonal part of a batched tensor.

#### View aliases


**Compat aliases for migration**

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

[`tf.compat.v1.raw_ops.MatrixDiagPartV2`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/MatrixDiagPartV2)

<br />

    tf.raw_ops.MatrixDiagPartV2(
        input, k, padding_value, name=None
    )

Returns a tensor with the `k[0]`-th to `k[1]`-th diagonals of the batched
`input`.

Assume `input` has `r` dimensions `[I, J, ..., L, M, N]`.
Let `max_diag_len` be the maximum length among all diagonals to be extracted,
`max_diag_len = min(M + min(k[1], 0), N + min(-k[0], 0))`
Let `num_diags` be the number of diagonals to extract,
`num_diags = k[1] - k[0] + 1`.

If `num_diags == 1`, the output tensor is of rank `r - 1` with shape
`[I, J, ..., L, max_diag_len]` and values:

    diagonal[i, j, ..., l, n]
      = input[i, j, ..., l, n+y, n+x] ; if 0 <= n+y < M and 0 <= n+x < N,
        padding_value                 ; otherwise.

where `y = max(-k[1], 0)`, `x = max(k[1], 0)`.

Otherwise, the output tensor has rank `r` with dimensions
`[I, J, ..., L, num_diags, max_diag_len]` with values:

    diagonal[i, j, ..., l, m, n]
      = input[i, j, ..., l, n+y, n+x] ; if 0 <= n+y < M and 0 <= n+x < N,
        padding_value                 ; otherwise.

where `d = k[1] - m`, `y = max(-d, 0)`, and `x = max(d, 0)`.

The input must be at least a matrix.

#### For example:

    input = np.array([[[1, 2, 3, 4],  # Input shape: (2, 3, 4)
                       [5, 6, 7, 8],
                       [9, 8, 7, 6]],
                      [[5, 4, 3, 2],
                       [1, 2, 3, 4],
                       [5, 6, 7, 8]]])

    # A main diagonal from each batch.
    tf.matrix_diag_part(input) ==> [[1, 6, 7],  # Output shape: (2, 3)
                                    [5, 2, 7]]

    # A superdiagonal from each batch.
    tf.matrix_diag_part(input, k = 1)
      ==> [[2, 7, 6],  # Output shape: (2, 3)
           [4, 3, 8]]

    # A tridiagonal band from each batch.
    tf.matrix_diag_part(input, k = (-1, 1))
      ==> [[[2, 7, 6],  # Output shape: (2, 3, 3)
            [1, 6, 7],
            [5, 8, 0]],
           [[4, 3, 8],
            [5, 2, 7],
            [1, 6, 0]]]

    # Padding value = 9
    tf.matrix_diag_part(input, k = (1, 3), padding_value = 9)
      ==> [[[4, 9, 9],  # Output shape: (2, 3, 3)
            [3, 8, 9],
            [2, 7, 6]],
           [[2, 9, 9],
            [3, 4, 9],
            [4, 3, 8]]]

<br />

<br />

<br />

| ## Args ||
|---|---|
| `input` | A `Tensor`. Rank `r` tensor where `r >= 2`. |
| `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]`. |
| `padding_value` | A `Tensor`. Must have the same type as `input`. The value to fill the area outside the specified diagonal band with. Default is 0. |
| `name` | A name for the operation (optional). |

<br />

<br />

<br />

<br />

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

<br />