class_docstr: Layer that computes a dot product between samples in two tensors.
  
  E.g. if applied to a list of two tensors `a` and `b` of shape
  `(batch_size, n)`, the output will be a tensor of shape `(batch_size, 1)`
  where each entry `i` will be the dot product between
  `a[i]` and `b[i]`.
  
  >>> x = np.arange(10).reshape(1, 5, 2)
  >>> print(x)
  [[[0 1]
    [2 3]
    [4 5]
    [6 7]
    [8 9]]]
  >>> y = np.arange(10, 20).reshape(1, 2, 5)
  >>> print(y)
  [[[10 11 12 13 14]
    [15 16 17 18 19]]]
  >>> tf.keras.layers.Dot(axes=(1, 2))([x, y])
  <tf.Tensor: shape=(1, 2, 2), dtype=int64, numpy=
  array([[[260, 360],
          [320, 445]]])>
  
  >>> x1 = tf.keras.layers.Dense(8)(np.arange(10).reshape(5, 2))
  >>> x2 = tf.keras.layers.Dense(8)(np.arange(10, 20).reshape(5, 2))
  >>> dotted = tf.keras.layers.Dot(axes=1)([x1, x2])
  >>> dotted.shape
  TensorShape([5, 1])
code: def compute_mask(self, inputs, mask=None):
      return None
  
imports: from tensorflow.python.keras import backend
  from tensorflow.python.keras.engine import base_layer_utils
  from tensorflow.python.keras.engine.base_layer import Layer
  from tensorflow.python.keras.utils import tf_utils
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import array_ops_stack
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import nn
label_desc: Use image node_id 5 for calling the Dot obj's underlying member method code with example usage: obj.compute_mask(inputs, mask) and returns: None