code: def CausalAttention(
      d_feature,
      n_heads=1,
      dropout=0.0,
      max_inference_length=2048,
      use_dconv=False,
      mode="train",
  ):
      """Returns a layer that maps activations to activations, with causal masking.
  
      Like :py:class:`Attention`, this layer type represents one pass of multi-head
      self-attention, but with causal masking rather than padding-based masking.
  
      Args:
        d_feature: Last/innermost dimension of activations in the input to and
            output from this layer.
        n_heads: Number of attention heads. Attention heads effectively split
            activation vectors into ``n_heads`` subvectors, of size
            ``d_feature / n_heads``.
        dropout: Probababilistic rate for attention dropout, which overrides
            (sets to zero) some attention strengths derived from query-key
            matching. As a result, on a given forward pass, some value vectors
            don't contribute to the output, analogous to how regular dropout can
            cause some node activations to be ignored. Applies only if layer is
            created in ``'train'`` mode.
        max_inference_length: Maximum sequence length allowed in non-training
            modes.
        use_dconv: if True, use depthwise convolutions on top of dense layers
          for Q, K and V.
        mode: One of ``'train'``, ``'eval'``, or ``'predict'``.
      """
      if d_feature % n_heads != 0:
          raise ValueError(
              f"Dimensionality of feature embedding ({d_feature}) is not a multiple "
              f"of the requested number of attention heads ({n_heads})."
          )
  
      def QKVLayer():
          """Function returning the Q, K and V layer."""
          if use_dconv:
              return cb.Serial(
                  core.Dense(d_feature),
                  convolution.CausalDepthwiseConv(),
              )
          else:
              return core.Dense(d_feature)
  
      return ConfigurableAttention(
          QKVLayer(),
          QKVLayer(),
          QKVLayer(),
          core.Dense(d_feature),
          n_heads=n_heads,
          qkv_attention_layer=DotProductCausalAttention(
              dropout=dropout,
              max_inference_length=max_inference_length,
              mode=mode,
          ),
      )
  
imports: import numpy
  from trax import fastmath
  from trax.fastmath import numpy
  from trax.layers import base
  from trax.layers import combinators
  from trax.layers import convolution
  from trax.layers import core
  from trax.layers import initializers
  from trax.layers.assert_shape import assert_shape
  from trax.layers.base import Fn
  from trax.layers.research import sparsity
label_desc: Use image node_id 7 for calling a global function with example usage: CausalAttention(d_feature, n_heads, dropout, max_inference_length, use_dconv, mode) and returns: ConfigurableAttention, cb, core