class_docstr: MultiHeadAttention layer.
  
  This is an implementation of multi-headed attention as described in the
  paper "Attention is all you Need"
  [Vaswani et al., 2017](https://arxiv.org/abs/1706.03762).
  If `query`, `key,` `value` are the same, then
  this is self-attention. Each timestep in `query` attends to the
  corresponding sequence in `key`, and returns a fixed-width vector.
  
  This layer first projects `query`, `key` and `value`. These are
  (effectively) a list of tensors of length `num_attention_heads`, where the
  corresponding shapes are `(batch_size, <query dimensions>, key_dim)`,
  `(batch_size, <key/value dimensions>, key_dim)`,
  `(batch_size, <key/value dimensions>, value_dim)`.
  
  Then, the query and key tensors are dot-producted and scaled. These are
  softmaxed to obtain attention probabilities. The value tensors are then
  interpolated by these probabilities, then concatenated back to a single
  tensor.
  
  Finally, the result tensor with the last dimension as `value_dim` can take
  a linear projection and return.
  
  Args:
      num_heads: Number of attention heads.
      key_dim: Size of each attention head for query and key.
      value_dim: Size of each attention head for value.
      dropout: Dropout probability.
      use_bias: Boolean, whether the dense layers use bias vectors/matrices.
      output_shape: The expected shape of an output tensor, besides the batch
          and sequence dims. If not specified, projects back to the query
          feature dim (the query input's last dimension).
      attention_axes: axes over which the attention is applied. `None` means
          attention over all axes, but batch, heads, and features.
      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 `(B, T, dim)`, where `B` is the batch size,
          `T` is the target sequence length, and dim is the feature dimension.
      value: Value tensor of shape `(B, S, dim)`, where `B` is the batch size,
          `S` is the source sequence length, and dim is the feature dimension.
      key: Optional key tensor of shape `(B, S, dim)`. If not given, will
          use `value` for both `key` and `value`, which is the most common
          case.
      attention_mask: a boolean mask of shape `(B, T, S)`, that prevents
          attention to certain positions. The boolean mask specifies which
          query elements can attend to which key elements, 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: The result of the computation, of shape `(B, T, E)`,
          where `T` is for target sequence shapes and `E` is the query input
          last dimension if `output_shape` is `None`. Otherwise, the
          multi-head outputs are projected to the shape specified by
          `output_shape`.
      attention_scores: (Optional) multi-head attention coefficients over
          attention axes.
code: def _masked_softmax(self, attention_scores, attention_mask=None):
      # Normalize the attention scores to probabilities.
      # attention_scores = [B, N, T, S]
      if attention_mask is not None:
          # The expand dim happens starting from the `num_heads` dimension,
          # (<batch_dims>, num_heads, <query_attention_dims,
          # key_attention_dims>)
          mask_expansion_axis = -len(self._attention_axes) * 2 - 1
          for _ in range(
              len(attention_scores.shape) - len(attention_mask.shape)
          ):
              attention_mask = ops.expand_dims(
                  attention_mask, axis=mask_expansion_axis
              )
      return self._softmax(attention_scores, mask=attention_mask)
  
imports: import collections
  import math
  import string
  import numpy
  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 18 for calling the MultiHeadAttention obj's underlying member method code with example usage: obj._masked_softmax(attention_scores, attention_mask) and returns: self