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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 dot_product_unmasked_attention_local_2d_tpu_simple( x, bias, total_key_depth, total_value_depth, num_heads, query_shape=(8, 8), dropout_rate=0.0, image_shapes=None, make_image_summary=False, dropout_broadcast_dims=None):
 """Calculate simple unmasked dot-product local self-attention 2d on tpu. The query, key, and value blocks are the same. We do not do a second linear transformation after computing the values Args: x: a Tensor with shape [batch, height, width, depth]. bias: bias Tensor. total_key_depth: the dimensions of the keys total_value_depth: the dimensions of the values num_heads: number of heads query_shape: a two tuple indicating query shape dropout_rate: a floating point number. image_shapes: optional tuple of integer scalars. make_image_summary: Whether to make an attention image summary. dropout_broadcast_dims: an optional list of integers less than 4 specifying in which dimensions to broadcast the dropout decisions. saves memory. Returns: ret: [batch, height, width, total_value_depth] tensor, the output of attention. q: [batch, height, width, total_key_depth] query tensor k: [batch, height, width, total_key_depth] key tensor v: [batch, height, width, total_value_depth] value tensor """

  # This calculation only works for self attention.
  # q, k and v must therefore have the same shape.
  orig_x_shape = common_layers.shape_list(x)
  # Pad query, key, value to ensure multiple of corresponding lengths if
  # necessary
  is_padded = False
  if (orig_x_shape[1]%query_shape[0]) != 0 or (
      orig_x_shape[2]%query_shape[1]) != 0:
    x = pad_to_multiple_2d(x, query_shape)
    is_padded = True
  _, height, width, depth = common_layers.shape_list(x)
  assert depth%num_heads == 0
  num_h_blocks = height//query_shape[0]
  num_w_blocks = width//query_shape[1]
  # Extract center queries, keys, and values
  x_blocks = _extract_blocks(x, query_shape[0], query_shape[1])
  x_blocks = tf.reshape(x_blocks, [-1, query_shape[0]*query_shape[1], depth])
  q, k, v = compute_qkv(x_blocks, None, total_key_depth, total_value_depth)
  hsplit = lambda x: split_heads(x, num_heads)
  q, k, v = map(hsplit, [q, k, v])
  logits = tf.matmul(q, k, transpose_b=True)
  if bias is not None:
    logits += bias
  weights = tf.nn.softmax(logits, name="attention_weights")
  # Dropping out the attention links for each of the heads
  weights = common_layers.dropout_with_broadcast_dims(
      weights, 1.0 - dropout_rate, broadcast_dims=dropout_broadcast_dims)
  if common_layers.should_generate_summaries() and make_image_summary:
    attention_image_summary(weights, image_shapes)
  output = tf.matmul(weights, v)
  output = combine_heads(output)
  # we need to get it back to shape [batch, height, width]
  ret = tf.reshape(output, [-1, num_h_blocks, num_w_blocks,
                            query_shape[0], query_shape[1], total_value_depth])

  ret = tf.transpose(ret, [0, 1, 3, 2, 4, 5])
  ret = tf.reshape(ret, [-1, num_h_blocks*query_shape[0],
                         num_w_blocks*query_shape[1], total_value_depth])
  # slice if padding was introduced
  if is_padded:
    ret = tf.slice(ret, [0, 0, 0, 0], [-1, orig_x_shape[1],
                                       orig_x_shape[2], -1])
  return ret, q, k, v