# tf.linalg.tensor_diag

Returns a diagonal tensor with a given diagonal values.

### Aliases:

• `tf.compat.v1.diag`
• `tf.compat.v1.linalg.tensor_diag`
• `tf.compat.v2.linalg.tensor_diag`
• `tf.diag`
• `tf.linalg.tensor_diag`
``````tf.linalg.tensor_diag(
diagonal,
name=None
)
``````

Defined in generated file: `python/ops/gen_array_ops.py`.

Given a `diagonal`, this operation returns a tensor with the `diagonal` and everything else padded with zeros. The diagonal is computed as follows:

Assume `diagonal` has dimensions [D1,..., Dk], then the output is a tensor of rank 2k with dimensions [D1,..., Dk, D1,..., Dk] where:

`output[i1,..., ik, i1,..., ik] = diagonal[i1, ..., ik]` and 0 everywhere else.

#### For example:

``````# 'diagonal' is [1, 2, 3, 4]
tf.diag(diagonal) ==> [[1, 0, 0, 0]
[0, 2, 0, 0]
[0, 0, 3, 0]
[0, 0, 0, 4]]
``````

#### Args:

• `diagonal`: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`. Rank k tensor where k is at most 1.
• `name`: A name for the operation (optional).

#### Returns:

A `Tensor`. Has the same type as `diagonal`.