// Returns a `llvm::DenseMap` which maps from the index of tensorlist in the
// result, to the index of the same tensorlist in the arguments. For `If` op's
// branch functions, the results and arguments are not usually matched 1-1. This
// will let us konw which tensorlist result maps to which tensorlist in the
// arguments. Once we know this info it will help us decide the types of the
// result tensorlist based on the operand's of the `If` op.

    }
  };

  while (!worklist.empty()) {
    tensorlist = worklist.top();
    worklist.pop();

    // TensorLists are semantically immutable. For example, TensorListSetItem
    // takes a TensorList as input and produces a TensorList as output. So to
    // traverse modifications to TensorList and verify that all elements written

  let summary =
    "Replicate TensorList init ops for correct shape assignments in shape inference";

  let description = [{
    If we pass same TensorList to a while op as multiple arguments or just use
    the same TensorList at multiple places and assign different
    TensorListSetItem to elements of TensorList, the shape inference is then

// Materialize the MlirPassthroughOp by replacing it with the MLIR module
// attached as an attribute.
std::unique_ptr<OperationPass<func::FuncOp>>
CreateMaterializePassthroughOpPass();

// Replicates the TensorList init op by undoing some CSE needed for correct
// shape assignment in shape_inference.
std::unique_ptr<OperationPass<func::FuncOp>>
CreateReplicateTensorListInitOpsPass();

  }];

  let arguments = (ins
    Arg<Variadic<TF_Float32Tensor>, [{A TensorList of gradients with which to update embedding tables.}]>:$gradients,
    Arg<Variadic<TF_Float32Tensor>, [{A TensorList of learning rates used for updating the embedding
tables via the optimizer. The length of the TensorList must be equal to the
number of dynamic learning rate tags specified in the

*   Others
    *   Retain parent namescope for ops added inside
        `tf.while_loop`/`tf.cond`/`tf.switch_case`.
    *   Update `tf.vectorized_map` to support vectorizing `tf.while_loop` and
        TensorList operations.
    *   `tf.custom_gradient` can now be applied to functions that accept nested
        structures of `tensors` as inputs (instead of just a list of tensors).