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# tf.raw_ops.Select

Selects elements from `x` or `y`, depending on `condition`.

The `x`, and `y` tensors must all have the same shape, and the output will also have that shape.

The `condition` tensor must be a scalar if `x` and `y` are scalars. If `x` and `y` are vectors or higher rank, then `condition` must be either a scalar, a vector with size matching the first dimension of `x`, or must have the same shape as `x`.

The `condition` tensor acts as a mask that chooses, based on the value at each element, whether the corresponding element / row in the output should be taken from `x` (if true) or `y` (if false).

If `condition` is a vector and `x` and `y` are higher rank matrices, then it chooses which row (outer dimension) to copy from `x` and `y`. If `condition` has the same shape as `x` and `y`, then it chooses which element to copy from `x` and `y`.

#### For example:

``````# 'condition' tensor is [[True,  False]
#                        [False, True]]
# 't' is [[1, 2],
#         [3, 4]]
# 'e' is [[5, 6],
#         [7, 8]]
select(condition, t, e)  # => [[1, 6], [7, 4]]

# 'condition' tensor is [True, False]
# 't' is [[1, 2],
#         [3, 4]]
# 'e' is [[5, 6],
#         [7, 8]]
select(condition, t, e) ==> [[1, 2],
[7, 8]]

``````

`condition` A `Tensor` of type `bool`.
`x` A `Tensor` which may have the same shape as `condition`. If `condition` is rank 1, `x` may have higher rank, but its first dimension must match the size of `condition`.
`y` A `Tensor` with the same type and shape as `x`.
`name` A name for the operation (optional).

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

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