<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def local_2d_self_attention_spatial_blocks(query_antecedent, kv_channels, heads, memory_h_dim=None, memory_w_dim=None, mask_right=False, master_dtype=tf.float32, slice_dtype=tf.float32, name=None):
 """Attention to the source position and a neighborhood to the left or right. The sequence is divided into blocks of length block_size. Attention for a given query position can only see memory positions less than or equal to the query position, in the corresponding block and the previous block. Args: query_antecedent: a mtf.Tensor with shape [batch, num_h_blocks, num_w_blocks, h_dim, w_dim, io_channels] must have the same size as query_length, but a different name. kv_channels: a mtf.Dimension (the size of the key and value vectors) heads: a mtf.Dimension (the number of heads) memory_h_dim: mtf Dimension, for the memory height block. memory_w_dim: mtf Dimension, for the memory width block. mask_right: bool, flag specifying whether we mask out attention to the right for the decoder. master_dtype: a tf.dtype slice_dtype: a tf.dtype name: an optional string. Returns: a Tensor of shape [batch, num_h_blocks, num_w_blocks, h_dim, w_dim, io_channels] Raises: ValueError: if channels or depth don't match. """

  with tf.variable_scope(
      name, default_name="multihead_attention", values=[query_antecedent]):

    h_dim, w_dim, io_channels = query_antecedent.shape.dims[-3:]
    batch, num_h_blocks, num_w_blocks = query_antecedent.shape.dims[:3]
    wq, wk, wv, wo = multihead_attention_vars(
        query_antecedent.mesh, heads, io_channels, kv_channels,
        master_dtype, slice_dtype, query_antecedent.dtype)

    # Rename dimensions for the memory height and width.
    memory_antecedent = mtf.rename_dimension(query_antecedent, h_dim.name,
                                             "memory_" + h_dim.name)
    memory_antecedent = mtf.rename_dimension(memory_antecedent, w_dim.name,
                                             "memory_" + w_dim.name)
    memory_h_dim, memory_w_dim = memory_antecedent.shape.dims[-3:-1]

    # Call einsum over the query and memory to get query q, keys k and values v.
    q = mtf.einsum([query_antecedent, wq],
                   mtf.Shape([
                       batch, heads, num_h_blocks, num_w_blocks, h_dim, w_dim,
                       kv_channels
                   ]))
    k = mtf.einsum([memory_antecedent, wk],
                   mtf.Shape([batch, heads, num_h_blocks, num_w_blocks,
                              memory_h_dim, memory_w_dim, kv_channels]))
    v = mtf.einsum([memory_antecedent, wv],
                   mtf.Shape([batch, heads, num_h_blocks, num_w_blocks,
                              memory_h_dim, memory_w_dim, kv_channels]))

    # Halo exchange for memory blocks.
    k, v = local_2d_halo_exchange(k, v, num_h_blocks, memory_h_dim,
                                  num_w_blocks, memory_w_dim, mask_right)

    # Calculate the causal mask to avoid peeking into the future. We compute
    # this once and reuse it for all blocks since the block_size is known.
    mask = None
    if mask_right:
      mask = attention_bias_local_2d_block(query_antecedent.mesh, h_dim, w_dim,
                                           memory_h_dim, memory_w_dim)

    output = dot_product_attention(q, k, v, mask=mask)

    return mtf.einsum(
        [output, wo],
        mtf.Shape(
            [batch, num_h_blocks, num_w_blocks, h_dim, w_dim, io_channels]))