     /// - Parameters:
     ///   - filterShape: The 3-D shape of the filter, representing
     ///     `[width, inputChannels, outputChannels]`.
-    ///   - stride: The stride of the sliding window for temporal dimension.
+    ///   - stride: The stride of the sliding window for the temporal dimension.
     ///   - padding: The padding algorithm for convolution.
     ///   - dilation: The dilation factor for the temporal dimension.
     ///   - activation: The element-wise activation function.

    /// - Returns: The output.
    @differentiable
    public func callAsFunction(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
        return activation(conv3D(input, filter: filter,
                                 strides: (1, strides.0, strides.1, strides.2, 1),
                                 padding: padding) + bias)
    }
}


    /// - Parameters:
    ///   - filterShape: The 3-D shape of the filter, representing
    ///     `[width, inputChannels, outputChannels]`.
    ///   - stride: The stride of the sliding window for the temporal dimension.
    ///   - padding: The padding algorithm for convolution.
    ///   - dilation: The dilation factor for the temporal dimension.
    ///   - activation: The element-wise activation function.
    ///   - generator: The random number generator for initialization.
    ///
