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### `tf.matrix_inverse(input, adjoint=None, name=None)` {#matrix_inverse}
Computes the inverse of one or more square invertible matrices or their
adjoints (conjugate transposes).
The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions
form square matrices. The output is a tensor of the same shape as the input
containing the inverse for all input submatrices `[..., :, :]`.
The op uses LU decomposition with partial pivoting to compute the inverses.
If a matrix is not invertible there is no guarantee what the op does. It
may detect the condition and raise an exception or it may simply return a
garbage result.
##### Args:
* <b>`input`</b>: A `Tensor`. Must be one of the following types: `float64`, `float32`.
Shape is `[..., M, M]`.
* <b>`adjoint`</b>: An optional `bool`. Defaults to `False`.
* <b>`name`</b>: A name for the operation (optional).
##### Returns:
A `Tensor`. Has the same type as `input`. Shape is `[..., M, M]`.
@compatibility(numpy)
Equivalent to np.linalg.inv
@end_compatibility
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