let hasVerifier = 1;
}

def TF_EinsumOp : TF_Op<"Einsum", [Pure]> {
  let summary = [{
Tensor contraction according to Einstein summation convention.
  }];

  let description = [{
Implements generalized Tensor contraction and reduction. Each input Tensor must
have a corresponding input subscript appearing in the comma-separated left-hand

Object-glasses, and thereby to make the Rings contract and dilate: The
Contraction or Dilatation of each Ring thus made by the variation of its
Colour was swiftest in the red, and slowest in the violet, and in the
intermediate Colours it had intermediate degrees of Celerity. Comparing
the quantity of Contraction and Dilatation made by all the degrees of
each Colour, I found that it was greatest in the red; less in the

        `batch_dims` case.
    *   Removing of dtype in the constructor of initializers and partition_info
        in call.
    *   Add `tf.math.nextafter` op.
    *   Turn on MKL-DNN contraction kernels by default. MKL-DNN dynamically
        dispatches the best kernel implementation based on CPU vector
        architecture. To disable them, build with
        `--define=tensorflow_mkldnn_contraction_kernel=0`.