// at a cost of 8 bytes.
//
// A miniNode is NOT a valid Node by itself: the embedding struct
// must at the least provide:
//
//	func (n *MyNode) String() string { return fmt.Sprint(n) }
//	func (n *MyNode) rawCopy() Node { c := *n; return &c }
//	func (n *MyNode) Format(s fmt.State, verb rune) { FmtNode(n, s, verb) }
//
// The embedding struct should also fill in n.op in its constructor,

def _composite_fully_connected(input_, filter_, bias, act):
  res = tf.raw_ops.MatMul(
      a=input_, b=filter_, transpose_a=False, transpose_b=True)
  res = tf.raw_ops.Add(x=res, y=bias)
  if act == 'RELU':
    return tf.raw_ops.Relu(features=res)
  elif act == 'RELU6':
    return tf.raw_ops.Relu6(features=res)
  elif act == 'TANH':
    return tf.raw_ops.Tanh(x=res)
  else:
    return res

*   Fixes a security vulnerability caused by lack of validation in
    `tf.raw_ops.DataFormatVecPermute` and `tf.raw_ops.DataFormatDimMap`
    ([CVE-2020-26267](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-26267))
*   Fixes a vulnerability caused by attempting to write to immutable memory
    region in `tf.raw_ops.ImmutableConst`
    ([CVE-2020-26268](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-26268)

    // safe.
    if (auto tensor_array_v3_op = dyn_cast<TF::TensorArrayV3Op>(tf_op)) {
      // The "flow" in TensorArrayV3 is always a scalar float tensor.
      // https://www.tensorflow.org/api_docs/python/tf/raw_ops/TensorArrayWriteV3
      Value flow = tensor_array_v3_op.getFlow();
      Type scalar_f32_tensor_type =
          RankedTensorType::get(/*shape=*/{}, rewriter.getF32Type());
      flow.setType(scalar_f32_tensor_type);

		}

		loc := fmt.Sprintf("%s%s.rules:%d", arch.name, suff, ruleLineno)
		for _, rule2 := range expandOr(rule) {
			r := Rule{Rule: rule2, Loc: loc}
			if rawop := strings.Split(rule2, " ")[0][1:]; isBlock(rawop, arch) {
				blockrules[rawop] = append(blockrules[rawop], r)
				continue
			}
			// Do fancier value op matching.
			match, _, _ := r.parse()
			op, oparch, _, _, _, _ := parseValue(match, arch, loc)

  def model_fn(args):
    # do something with dynamic tensor

  @function.Defun(capture_resource_var_by_value=False)
  def tpu_subgraph():
      return tf.tpu.rewrite(model_fn, args)

  return tf.raw_ops.TPUPartitionedCall(
      args=tpu_subgraph.captured_inputs,
      Tout=[o.type for o in tpu_subgraph.definition.signature.output_arg],
      f=tpu_subgraph,
      device_ordinal=[0])

elements in `x` must be complex numbers of the form \\(a + bj\\). The absolute
value is computed as \\( \sqrt{a^2 + b^2}\\).

For example:

>>> x = tf.complex(3.0, 4.0)
>>> print((tf.raw_ops.ComplexAbs(x=x, Tout=tf.dtypes.float32, name=None)).numpy())
5.0
  }];

  let arguments = (ins
    TensorOf<[TF_Complex128, TF_Complex64]>:$x
  );

  let results = (outs

// AllToAll op legalizations.
//===----------------------------------------------------------------------===//

// -----

// CHECK-LABEL: func @alltoall_basic
// See https://www.tensorflow.org/api_docs/python/tf/raw_ops/AllToAll
func.func @alltoall_basic(%input: tensor<1x2xf32>) -> tensor<2x1xf32> {
  %group_assignment = "tf.Const" () {
    value = dense<[[0, 1]]> : tensor<1x2xi32>
  } : () -> tensor<1x2xi32>