class_docstr: Grouped Query Attention layer.
  
  This is an implementation of grouped-query attention introduced by
  [Ainslie et al., 2023](https://arxiv.org/abs/2305.13245). Here
  `num_key_value_heads` denotes number of groups, setting
  `num_key_value_heads` to 1 is equivalent to multi-query attention, and
  when `num_key_value_heads` is equal to `num_query_heads` it is equivalent
  to multi-head attention.
  
  This layer first projects `query`, `key`, and `value` tensors. Then, `key`
  and `value` are repeated to match the number of heads of `query`.
  
  Then, the `query` is scaled and dot-producted with `key` tensors. These are
  softmaxed to obtain attention probabilities. The value tensors are then
  interpolated by these probabilities and concatenated back to a single
  tensor.
  
  Args:
      head_dim: Size of each attention head.
      num_query_heads: Number of query attention heads.
      num_key_value_heads: Number of key and value attention heads.
      dropout: Dropout probability.
      use_bias: Boolean, whether the dense layers use bias vectors/matrices.
      kernel_initializer: Initializer for dense layer kernels.
      bias_initializer: Initializer for dense layer biases.
      kernel_regularizer: Regularizer for dense layer kernels.
      bias_regularizer: Regularizer for dense layer biases.
      activity_regularizer: Regularizer for dense layer activity.
      kernel_constraint: Constraint for dense layer kernels.
      bias_constraint: Constraint for dense layer kernels.
  
  Call arguments:
      query: Query tensor of shape `(batch_dim, target_seq_len, feature_dim)`,
          where `batch_dim` is batch size, `target_seq_len` is the length of
          target sequence, and `feature_dim` is dimension of feature.
      value: Value tensor of shape `(batch_dim, source_seq_len, feature_dim)`,
          where `batch_dim` is batch size, `source_seq_len` is the length of
          source sequence, and `feature_dim` is dimension of feature.
      key: Optional key tensor of shape
          `(batch_dim, source_seq_len, feature_dim)`. If not given, will use
          `value` for both `key` and `value`, which is most common case.
      attention_mask: A boolean mask of shape
          `(batch_dim, target_seq_len, source_seq_len)`, that prevents
          attention to certain positions. The boolean mask specifies which
          query elements can attend to which key elements, where 1 indicates
          attention and 0 indicates no attention. Broadcasting can happen for
          the missing batch dimensions and the head dimension.
      return_attention_scores: A boolean to indicate whether the output
          should be `(attention_output, attention_scores)` if `True`, or
          `attention_output` if `False`. Defaults to `False`.
      training: Python boolean indicating whether the layer should behave in
          training mode (adding dropout) or in inference mode (no dropout).
          Will go with either using the training mode of the parent
          layer/model or `False` (inference) if there is no parent layer.
      use_causal_mask: A boolean to indicate whether to apply a causal mask to
          prevent tokens from attending to future tokens (e.g., used in a
          decoder Transformer).
  
  Returns:
      attention_output: Result of the computation, of shape
          `(batch_dim, target_seq_len, feature_dim)`, where `target_seq_len`
          is for target sequence length and `feature_dim` is the query input
          last dim.
      attention_scores: (Optional) attention coefficients of shape
          `(batch_dim, num_query_heads, target_seq_len, source_seq_len)`.
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code: def _get_common_kwargs_for_sublayer(self):
      common_kwargs = dict(
          kernel_regularizer=self.kernel_regularizer,
          bias_regularizer=self.bias_regularizer,
          activity_regularizer=self.activity_regularizer,
          kernel_constraint=self.kernel_constraint,
          bias_constraint=self.bias_constraint,
          dtype=self.dtype_policy,
      )
      # Create new clone of kernel/bias initializer, so that we don't reuse
      # the initializer instance, which could lead to same init value since
      # initializer is stateless.
      kernel_initializer = (
          self.kernel_initializer.__class__.from_config(
              self.kernel_initializer.get_config()
          )
      )
      bias_initializer = self.bias_initializer.__class__.from_config(
          self.bias_initializer.get_config()
      )
      common_kwargs["kernel_initializer"] = kernel_initializer
      common_kwargs["bias_initializer"] = bias_initializer
      return common_kwargs
  
imports: from keras import constraints
  from keras import initializers
  from keras import ops
  from keras import regularizers
  from keras.api_export import keras_export
  from keras.layers.activations.softmax import Softmax
  from keras.layers.core.einsum_dense import EinsumDense
  from keras.layers.layer import Layer
  from keras.layers.regularization.dropout import Dropout
label_desc: Use image node_id 3 for calling the GroupedQueryAttention obj's underlying member method code with example usage: obj._get_common_kwargs_for_sublayer() and returns: common_kwargs