Clustered nodes: 2385
Unclustered nodes: 4221
Number of clusters: 30

unclustered size 4221
 Add 17
 AddN 1
 All 1
 ApplyAdam 38
 Assert 7
 Assign 47
 AssignAdd 2
 AssignSub 2
 BroadcastGradientArgs 44
 Cast 38
 ConcatV2 3
 Const 875
 ControlTrigger 5
 Enter 874
 Equal 4
 Exit 69
 ExpandDims 9
 Fill 5
 FloorMod 1
 GreaterEqual 7
 Identity 113
 IsVariableInitialized 1
 IteratorGetNext 1

module attributes {tf.versions = {producer = 179 : i32}} {
  func.func @main() -> (tensor<0xi32>, tensor<0xi32>) {
    %0 = "tf.Const"() {value = dense<[]> : tensor<0xi32>} : () -> tensor<0xi32>
    %r0, %r1 = "tf.BroadcastGradientArgs"(%0, %0) {T = i32} : (tensor<0xi32>, tensor<0xi32>) -> (tensor<0xi32>, tensor<0xi32>)
    func.return %r0, %r1 : tensor<0xi32>, tensor<0xi32>
  }
}

// CHECK-LABEL: HloModule main

  elif act == 'TANH':
    y = math_ops.conj(y)
    grad = gen_math_ops.tanh_grad(y, grad)

  broadcast_shape = tf.shape(y)
  input_value_shape = tf.shape(op.inputs[2])
  _, reduction_axes = tf.raw_ops.BroadcastGradientArgs(
      s0=broadcast_shape, s1=input_value_shape)
  updates_grad_reshaped = tf.reduce_sum(
      grad, axis=reduction_axes, keepdims=True)
  bias_grad = tf.reshape(updates_grad_reshaped, input_value_shape)