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### `tf.nn.conv3d(input, filter, strides, padding, name=None)` {#conv3d}
Computes a 3-D convolution given 5-D `input` and `filter` tensors.
In signal processing, cross-correlation is a measure of similarity of
two waveforms as a function of a time-lag applied to one of them. This
is also known as a sliding dot product or sliding inner-product.
Our Conv3D implements a form of cross-correlation.
##### Args:
* <b>`input`</b>: A `Output`. Must be one of the following types: `float32`, `float64`, `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`, `complex128`, `qint8`, `quint8`, `qint32`, `half`.
Shape `[batch, in_depth, in_height, in_width, in_channels]`.
* <b>`filter`</b>: A `Output`. Must have the same type as `input`.
Shape `[filter_depth, filter_height, filter_width, in_channels,
out_channels]`. `in_channels` must match between `input` and `filter`.
* <b>`strides`</b>: A list of `ints` that has length `>= 5`.
1-D tensor of length 5. The stride of the sliding window for each
dimension of `input`. Must have `strides[0] = strides[4] = 1`.
* <b>`padding`</b>: A `string` from: `"SAME", "VALID"`.
The type of padding algorithm to use.
* <b>`name`</b>: A name for the operation (optional).
##### Returns:
A `Output`. Has the same type as `input`.
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