[null,null,["Last updated 2020-10-01 UTC."],[],[],null,["# tf.contrib.distributions.tridiag\n\n\u003cbr /\u003e\n\n|---------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/ops/distributions/util.py#L981-L1046) |\n\nCreates a matrix with values set above, below, and on the diagonal. \n\n tf.contrib.distributions.tridiag(\n below=None, diag=None, above=None, name=None\n )\n\n#### Example:\n\n tridiag(below=[1., 2., 3.],\n diag=[4., 5., 6., 7.],\n above=[8., 9., 10.])\n # ==\u003e array([[ 4., 8., 0., 0.],\n # [ 1., 5., 9., 0.],\n # [ 0., 2., 6., 10.],\n # [ 0., 0., 3., 7.]], dtype=float32)\n\n| **Warning:** This Op is intended for convenience, not efficiency.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|---------|---------------------------------------------------------------------------------------------------------------------------------|\n| `below` | `Tensor` of shape `[B1, ..., Bb, d-1]` corresponding to the below diagonal part. `None` is logically equivalent to `below = 0`. |\n| `diag` | `Tensor` of shape `[B1, ..., Bb, d]` corresponding to the diagonal part. `None` is logically equivalent to `diag = 0`. |\n| `above` | `Tensor` of shape `[B1, ..., Bb, d-1]` corresponding to the above diagonal part. `None` is logically equivalent to `above = 0`. |\n| `name` | Python `str`. The name to give this op. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|-----------|------------------------------------------------------------|\n| `tridiag` | `Tensor` with values set above, below and on the diagonal. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Raises ------ ||\n|--------------|---------------------------|\n| `ValueError` | if all inputs are `None`. |\n\n\u003cbr /\u003e"]]