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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 attention_params(context, kv_dim, num_heads, num_memory_heads=0, shared_kv=False):
 """Attention Parameters for Transformer Layers. The num_heads argument indicates the number of read-heads. For the familiar behavior described in "Attention Is All You Need", set num_memory_heads=0. If num_memory_heads==1, then there is only a single write-head, and multiple read-heads. This leads to faster incremental decoding, since the recurrent state is smaller If num_memory_heads > 1, then num_memory_heads indicates the number of write-heads. A fraction of the read-heads read each write-head. num_memory_heads must divide num_heads. This behavior has not yet been tested. Args: context: a transformer.Context kv_dim: a dimension (for key and value channels) num_heads: an integer num_memory_heads: an optional integer shared_kv: a boolean Returns: an attention.AttentionParams object """

  if num_heads == 1:
    query_heads_dims = None
    memory_heads_dims = None
  elif num_memory_heads == 0:
    query_heads_dims = [mtf.Dimension("heads", num_heads)]
    memory_heads_dims = query_heads_dims
  elif num_memory_heads == 1:
    query_heads_dims = [mtf.Dimension("heads", num_heads)]
    memory_heads_dims = None
  else:
    if num_heads % num_memory_heads != 0:
      raise ValueError("num_memory_heads must divide num_heads")
    memory_heads_dims = [mtf.Dimension("heads", num_memory_heads)]
    query_heads_dims = memory_heads_dims + [
        mtf.Dimension("query_heads", num_heads // num_memory_heads)]
  return attention.AttentionParams(
      context.mesh,
      query_input_dim=context.model_dim,
      memory_input_dim=context.model_dim,
      output_dim=context.model_dim,
      key_dim=kv_dim,
      value_dim=kv_dim,
      query_heads_dims=query_heads_dims,
      memory_heads_dims=memory_heads_dims,
      variable_dtype=context.variable_dtype,
      shared_kv=shared_kv)