message XlaAutoClusteringSummary {
  // Represents a single element in a histogram of ops ("op" as in "TensorFlow
  // operation").
  //
  // Next ID: 3
  message OpAndCount {
    // The TensorFlow operation (like MatMult, Add etc.)
    string op = 1;

    // The number of times this occurs.
    int32 count = 2;
  }

  // Describes a single XLA cluster.
  //
  // Next ID: 4
  message Cluster {
    string name = 1;

Christian Sigg <******@****.***> 1714640622 -0700

# CHECK-NEXT: %[[READ_VAR_0:.*]] = "tf.ReadVariableOp"(%[[ARG_2]]) {{{.*}}} : (tensor<!tf_type.resource<tensor<1x3xf32>>>) -> tensor<1x3xf32>
# CHECK-NEXT: %[[MATMUL_0:.*]] = "tf.MatMul"(%[[ARG_1]], %[[READ_VAR_0]]) <{{{.*}}}> {{{.*}}} : (tensor<3x1xf32>, tensor<1x3xf32>) -> tensor<3x3xf32>
# CHECK-NEXT: return %[[MATMUL_0]] : tensor<3x3xf32>


def Test():
  x = tf.constant([[1.0], [1.0], [1.0]])
  y = tf.compat.v1.get_variable(
      name='y',

    TFE_OpAddInput(matmul, h0_task0, status);
    ASSERT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);
    TFE_OpAddInput(matmul, has_packed_input ? packed_handle : h1_task2, status);
    ASSERT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);
  } else {
    // Handles are on task0 (local), and task2, but op is on task1.
    matmul = MatMulOp(ctx, h0_task0, h1_task2);
  }
  if (remote) {

  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  TFE_Op* matmul = MatMulOp(ctx, h0_task1, h1_task1);
  TFE_OpSetDevice(matmul, remote_device_name, status);
  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  TFE_TensorHandle* retvals[1];
  int num_retvals = 1;
  TFE_Execute(matmul, &retvals[0], &num_retvals, status);
  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

      "/job:localhost/replica:0/task:0/device:CPU:0");
  Output a = ops::Placeholder(root.WithOpName("A"), DT_FLOAT);
  for (int i = 0; i < 5; i++) {
    a = ops::MatMul(root.WithOpName(absl::StrCat("matmul_", i)), a, a);
    a = ops::Add(root.WithOpName(absl::StrCat("add_", i)), a, a);
  }

  GraphDef graph_def;
  root.graph()->ToGraphDef(&graph_def);
  return graph_def;
}

func.func @fold_identity_n_test(%arg0: tensor<2x2xf32>, %arg1: tensor<2x2xf32>) -> (tensor<2x2xf32>, tensor<2x2xf32>) {
  // CHECK: tf.MatMul
  %outputs = "tf.MatMul"(%arg0, %arg1) {device = "TPU", transpose_a = false, transpose_b = false} : (tensor<2x2xf32>, tensor<2x2xf32>) -> tensor<2x2xf32>
  %outputs_0 = "tf.MatMul"(%arg0, %arg1) {device = "TPU", transpose_a = false, transpose_b = false} : (tensor<2x2xf32>, tensor<2x2xf32>) -> tensor<2x2xf32>

  bias_grad = tf.reshape(updates_grad_reshaped, input_value_shape)

  a = math_ops.conj(op.inputs[0])
  b = math_ops.conj(op.inputs[1])
  grad_a = gen_math_ops.mat_mul(grad, b)
  grad_b = gen_math_ops.mat_mul(grad, a, transpose_a=True)
  return [grad_a, grad_b, bias_grad]


@Composite(
    'NewMaxPool',
    inputs=['input_: T'],
    attrs=[

# CHECK-DAG: %[[MUL1:.*]] = "tf.MatMul"(%[[ARG0]], %[[ARG1]])
# CHECK-DAG: %[[MUL2:.*]] = "tf.MatMul"(%[[ARG1]], %[[ARG0]])
# CHECK:  %[[IDENTITY:.*]]:2 = "tf.IdentityN"(%[[MUL1]], %[[MUL2]])
# CHECK: return %[[IDENTITY]]#1, %[[IDENTITY]]#0


def Test():

  x = tf.constant(1.0, shape=(5, 3))
  y = tf.constant(1.0, shape=(3, 5))

  s = tf.matmul(x, y)
  t = tf.matmul(y, x)
  [t, s] = array_ops.identity_n([t, s])

  op: "Identity"
  input: "Placeholder_1"
  attr {
    key: "T"
    value {
      type: DT_BOOL
    }
  }
}
node {
  name: "cond/MatMul"
  op: "MatMul"
  input: "cond/MatMul/Switch:1"
  input: "cond/MatMul/Switch_1:1"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "transpose_a"
    value {
      b: false
    }