human
Complete the below
def local_attention_1d(q, k, v, block_length=128, filter_width=100, name=None):

    ''''strided block local self-attention.
    Args:
    q: a Tensor with shape [batch, heads, length, depth_k]
    k: a Tensor with shape [batch, heads, length, depth_k]
    v: a Tensor with shape [batch, heads, length, depth_v]
    block_length: an integer
    filter_width: an integer indicating how much to look left.
    name: an optional string
    Returns:
    a Tensor of shape [batch, heads, length, depth_v]'
    '''

What is a the Python program you requested?
assistant
#Here is the Python program you requested.
    with tf.variable_scope(name, default_name='local_self_attention_1d', values=[q, k, v]):
        v_shape = v.get_shape()
        depth_v = tf.shape(v)[3]
        batch_size = tf.shape(q)[0]
        num_heads = tf.shape(q)[1]
        original_length = tf.shape(q)[2]
        def pad_to_multiple(x, pad_length):
            x_length = tf.shape(x)[2]
            return tf.pad(x, [[0, 0], [0, 0], [0, ((- x_length) % pad_length)], [0, 0]])
        def pad_l_and_r(x, pad_length):
            return tf.pad(x, [[0, 0], [0, 0], [pad_length, pad_length], [0, 0]])
        q = pad_to_multiple(q, block_length)
        k = pad_to_multiple(k, block_length)
        v = pad_to_multiple(v, block_length)
        new_q_shape = tf.shape(q)
        q = tf.reshape(q, [new_q_shape[0], new_q_shape[1], (new_q_shape[2] // block_length), block_length, new_q_shape[3]])
        k = pad_l_and_r(k, filter_width)
        v = pad_l_and_r(v, filter_width)
        length = tf.shape(k)[2]
        full_filter_width = (block_length + (2 * filter_width))
        indices = tf.range(0, length, delta=1, name='index_range')
        indices = tf.reshape(indices, [1, (-1), 1])
        kernel = tf.expand_dims(tf.eye(full_filter_width), axis=1)
        gather_indices = tf.nn.conv1d(tf.cast(indices, tf.float32), kernel, block_length, padding='VALID', name='gather_conv')
        gather_indices = tf.squeeze(tf.cast(gather_indices, tf.int32), axis=0)
        k_t = tf.transpose(k, [2, 0, 1, 3])
        k_new = tf.gather(k_t, gather_indices)
        k_new = tf.transpose(k_new, [2, 3, 0, 1, 4])
        attention_bias = tf.expand_dims((embedding_to_padding(k_new) * (-1000000000.0)), axis=(-2))
        v_t = tf.transpose(v, [2, 0, 1, 3])
        v_new = tf.gather(v_t, gather_indices)
        v_new = tf.transpose(v_new, [2, 3, 0, 1, 4])
        logits = tf.matmul(q, k_new, transpose_b=True)
        attention = tf.nn.softmax((logits + attention_bias))
        output = tf.matmul(attention, v_new)
        output = tf.reshape(output, [batch_size, num_heads, (-1),...