let summary = "Batch Matrix Multiply Operator";

  let description = [{
Performs a batched matrix multiplication on the inputs. Follows the
conventions of TensorFlow BatchMatMulV2, with support for unknown dimensions
in the batch dimensions and broadcasting.

    Inputs:
      `inputs[0]`: required: input LHS
      `inputs[1]`: required: input RHS

                                           Value *out_lhs, Value *out_rhs,
                                           PatternRewriter *rewriter) {
  // The dimension structure of the relevant operands to a tf.BatchMatMulV2 is:
  // - lhs: [LHSBATCHDIMS..., LHSROWS, LHSCOLS]
  // - rhs: [RHSBATCHDIMS..., RHSROWS, RHSCOLS]
  // - result: [broadcast(LHSBATCHDIMS, RHSBATCHDIMS)..., LHSROWS, RHSCOLS]

  );

  TF_DerivedOperandTypeAttr T = TF_DerivedOperandTypeAttr<0>;

  let hasCanonicalizer = 1;

  let hasVerifier = 1;
}

def TF_BatchMatMulV2Op : TF_Op<"BatchMatMulV2", [Pure, TF_SameOperandsAndResultElementTypeResolveRef]> {
  let summary = "Multiplies slices of two tensors in batches.";

  let description = [{
Multiplies all slices of `Tensor` `x` and `y` (each slice can be

    *   Remove unused `StringViewVariantWrapper`.
    *   Delete unused `Fingerprint64Map` op registration
    *   Add broadcasting support to `tf.matmul`.
    *   Add C++ Gradient for `BatchMatMulV2`.
    *   Add `tf.math.cumulative_logsumexp` operation.
    *   Add ellipsis (...) support for `tf.einsum()`.
    *   Add expand_composites argument to all `nest.*` methods.
    *   Added `strings.byte_split`.