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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def visualize_conv_activations(activation, name):
 """Visualize activations for convolution layers. Remarks: This tries to place all activations into a square. Args: activation: tensor with the activation [B,H,W,C] name: label for tensorboard Returns: image of almost all activations """

    import math
    with tf.name_scope('visualize_act_' + name):
        _, h, w, c = activation.get_shape().as_list()
        rows = []
        c_per_row = int(math.sqrt(c))
        for y in range(0, c - c_per_row, c_per_row):
            row = activation[:, :, :, y:y + c_per_row]  # [?, H, W, 32] --> [?, H, W, 5]
            cols = tf.unstack(row, axis=3)              # [?, H, W, 5] --> 5 * [?, H, W]
            row = tf.concat(cols, 1)
            rows.append(row)

        viz = tf.concat(rows, 2)
    tf.summary.image('visualize_act_' + name, tf.expand_dims(viz, -1))