"composite" tensors, such as `tf.RaggedTensor`, as inputs.
    *   Fix device placement issues related to datasets with ragged tensors of
        strings (i.e. variant encoded data with types not supported on GPU).
    *   'experimental_follow_type_hints' for tf.function has been deprecated.
        Please use input_signature or reduce_retracing to minimize retracing.

  INT16 = 7,
  COMPLEX64 = 8,
  INT8 = 9,
  FLOAT64 = 10,
  COMPLEX128 = 11,
  UINT64 = 12,
  // Experimental: Resource and variant types are experimental, that are subject
  // to change. Do not implement custom kernels using resource & variant types
  // now.
  RESOURCE = 13,
  VARIANT = 14,
  UINT32 = 15,
  UINT16 = 16,
  INT4 = 17,
  BFLOAT16 = 18,
}

  INT16 = 7,
  COMPLEX64 = 8,
  INT8 = 9,
  FLOAT64 = 10,
  COMPLEX128 = 11,
  UINT64 = 12,
  // Experimental: Resource and variant types are experimental, that are subject
  // to change. Do not implement custom kernels using resource & variant types
  // now.
  RESOURCE = 13,
  VARIANT = 14,
  UINT32 = 15,
}

// Custom quantization parameters for experimenting with new quantization
// techniques.

  %1 = "tf.TensorListGetItem"(%0, %arg2, %arg1) {device = "/job:localhost/replica:0/task:0/device:GPU:0"} : (tensor<!tf_type.variant<tensor<1x32xf32>>>, tensor<i32>, tensor<2xi32>) -> tensor<1x32xf32>

Creates a dataset that invokes its 'next' region to generate elements. Conceptually,
within MLIR, we treat this op as if it fills a buffer with all the results right away,
and those results are then passed (through the variant tensor result) to
MakeIterator / IteratorGetNext. Note that the actual TF implementation differs: It
generates the next element just in time, during IteratorGetNext.

init_extra_args: Additional arguments to pass to 'init'.

func.func @addN_variant(%arg0: tensor<!tf_type.variant<tensor<2xf32>>>, %arg1: tensor<!tf_type.variant<tensor<2xf32>>>, %arg2: tensor<!tf_type.variant<tensor<2xf32>>>) -> tensor<!tf_type.variant<tensor<2xf32>>> {
  // CHECK: tf.AddN
  %0 = "tf.AddN"(%arg0, %arg1, %arg2) : (tensor<!tf_type.variant<tensor<2xf32>>>, tensor<!tf_type.variant<tensor<2xf32>>>, tensor<!tf_type.variant<tensor<2xf32>>>) -> tensor<!tf_type.variant<tensor<2xf32>>>

  let summary = [{
Converts the given variant tensor to an iterator and stores it in the given resource.
  }];

  let arguments = (ins
    Arg<TF_ResourceTensor, [{A handle to an iterator resource.}], [TF_DatasetIteratorWrite]>:$resource_handle,
    Arg<TF_VariantTensor, [{A variant tensor storing the state of the iterator contained in the
resource.}]>:$serialized
  );