class_docstr: A densely connected layer that uses tf.einsum as the backing computation.
  
  This layer can perform einsum calculations of arbitrary dimensionality.
  
  Attributes:
    output_shape: Positive integer or tuple, dimensionality of the output space.
    num_summed_dimensions: The number of dimensions to sum over. Standard 2D
      matmul should use 1, 3D matmul should use 2, and so forth.
    activation: Activation function to use. If you don't specify anything, no
      activation is applied
      (ie. "linear" activation: `a(x) = x`).
    use_bias: Boolean, whether the layer uses a bias vector.
    kernel_initializer: Initializer for the `kernel` weights matrix.
    bias_initializer: Initializer for the bias vector.
    kernel_regularizer: Regularizer function applied to the `kernel` weights
      matrix.
    bias_regularizer: Regularizer function applied to the bias vector.
    activity_regularizer: Regularizer function applied to the output of the
      layer (its "activation")..
    kernel_constraint: Constraint function applied to the `kernel` weights
      matrix.
    bias_constraint: Constraint function applied to the bias vector.
  Input shape:
    N-D tensor with shape: `(batch_size, ..., input_dim)`. The most common
      situation would be a 2D input with shape `(batch_size, input_dim)`.
  Output shape:
    N-D tensor with shape: `(batch_size, ..., units)`. For instance, for a 2D
      input with shape `(batch_size, input_dim)`, the output would have shape
      `(batch_size, units)`.
code: def get_config(self):
      config = {
          "output_shape": self._output_shape,
          "activation": tf.keras.activations.serialize(
              self._activation
          ),
          "use_bias": self._use_bias,
          "kernel_initializer": tf.keras.initializers.serialize(
              self._kernel_initializer
          ),
          "bias_initializer": tf.keras.initializers.serialize(
              self._bias_initializer
          ),
          "kernel_regularizer": tf.keras.regularizers.serialize(
              self._kernel_regularizer
          ),
          "bias_regularizer": tf.keras.regularizers.serialize(
              self._bias_regularizer
          ),
          "activity_regularizer": tf.keras.regularizers.serialize(
              self._activity_regularizer
          ),
          "kernel_constraint": tf.keras.constraints.serialize(
              self._kernel_constraint
          ),
          "bias_constraint": tf.keras.constraints.serialize(
              self._bias_constraint
          ),
      }
      base_config = super(DenseEinsum, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
file_path: autodist/examples/benchmark/utils/modeling/layers/dense_einsum.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
label_desc: Use image node_id 5 for calling the DenseEinsum obj's underlying member method code with example usage: obj.get_config() and returns: dict