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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 multihead_self_attention_memory_compressed(x, mask_right, compression_factor, kv_channels, heads, dropout=0.0, dropout_broadcast_dims=None, master_dtype=tf.float32, slice_dtype=tf.float32, name="multihead_attention"):
 """Memory-compressed self-attention. The memory is first average-pooled (strided) to make it shorter by a factor of compression_factor. Args: x: a mtf.Tensor with shape [<batch_dims>, query_length, io_channels] mask_right: a boolean compression_factor: an integer kv_channels: a mtf.Dimension (the size of the key and value vectors) heads: a mtf.Dimension (the number of heads) dropout: a floating point value dropout_broadcast_dims: an optional list of mtf.Dimension master_dtype: a tf.dtype slice_dtype: a tf.dtype name: an optional string. Returns: A mtf.Tensor with shape [batch, query_length, io_channels] Raises: ValueError: if the dimensions do not match. """

  batch_dims = x.shape.dims[:-2]
  length, io_channels = x.shape.dims[-2:]
  with tf.variable_scope(name,
                         default_name="compressed_attention",
                         values=[x]):
    wq, wk, wv, wo = multihead_attention_vars(
        x.mesh, heads, io_channels, kv_channels,
        master_dtype, slice_dtype, x.dtype)
    memory_antecedent = compress_mean(x, length, compression_factor)
    memory_antecedent = rename_length_to_memory_length(memory_antecedent)
    memory_length = memory_antecedent.shape.dims[-2]
    q = mtf.einsum(
        [x, wq],
        mtf.Shape(batch_dims + [heads, length, kv_channels]))
    k = mtf.einsum(
        [memory_antecedent, wk],
        mtf.Shape(batch_dims + [heads, memory_length, kv_channels]))
    v = mtf.einsum(
        [memory_antecedent, wv],
        mtf.Shape(batch_dims + [heads, memory_length, kv_channels]))
    if mask_right:
      query_pos = mtf.range(x.mesh, length, dtype=tf.int32)
      memory_pos = (
          mtf.range(x.mesh, memory_length, dtype=tf.int32) * compression_factor
          + (compression_factor - 1))
      mask = mtf.cast(mtf.greater(memory_pos, query_pos), x.dtype) * -1e9
    else:
      mask = None
    o = dot_product_attention(
        q, k, v, mask, dropout, dropout_broadcast_dims, extra_logit=0.0)
    return mtf.einsum(
        [o, wo], mtf.Shape(batch_dims + [length, io_channels]))