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 _build_einsum_string(
      self, free_input_dims, bound_dims, output_dims
  ):
      input_str = ""
      kernel_str = ""
      output_str = ""
      letter_offset = 0
      for i in range(free_input_dims):
          char = _CHR_IDX[i + letter_offset]
          input_str += char
          output_str += char
  
      letter_offset += free_input_dims
      for i in range(bound_dims):
          char = _CHR_IDX[i + letter_offset]
          input_str += char
          kernel_str += char
  
      letter_offset += bound_dims
      for i in range(output_dims):
          char = _CHR_IDX[i + letter_offset]
          kernel_str += char
          output_str += char
  
      return input_str + "," + kernel_str + "->" + output_str
  
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 2 for calling the DenseEinsum obj's underlying member method code with example usage: obj._build_einsum_string(free_input_dims, bound_dims, output_dims) and returns: unknown