}
  attr {
    key: "num_bits"
    value {
      i: 8
    }
  }
}
node {
  name: "sequential/quant_dense_1/iquant/IdentityN"
  op: "IdentityN"
  input: "sequential/quant_dense_1/iquant/FakeQuantWithMinMaxVars"
  input: "sequential/quant_dense/oquant/IdentityN"
  attr {
    key: "T"
    value {
      list {
        type: DT_FLOAT
        type: DT_FLOAT
      }
    }
  }

  %3 = "tf.IdentityN"(%2) {device = ""} : (tensor<1x2xf32>) -> tensor<1x2xf32>
  return %3 : tensor<1x2xf32>
}

// CHECK: func @cast_bf16_matmul_to_fp32
// CHECK-DAG: %[[cst:.*]] = "tf.Const"() <{value = dense<{{.*}}> : tensor<10x2xf32>}> : () -> tensor<10x2xf32>
// CHECK: %[[matmul:.*]] = "tf.MatMul"(%arg0, %[[cst]])
// CHECK: %[[identity:.*]] = "tf.IdentityN"(%[[matmul]])

    }
  }
}
node {
  name: "Identity"
  op: "Identity"
  input: "cond"
  device: "/device:TPU_REPLICATED_CORE:0"
  attr {
    key: "T"
    value {
      type: DT_INT32
    }
  }
  attr {
    key: "_tpu_replicate"
    value {
      s: "cluster"
    }
  }
}
node {
  name: "Identity_1"
  op: "Identity"
  input: "cond:1"

// RUN: tf-opt %s -tf-tpu-identity-pruning | FileCheck %s --dump-input=always

// Tests Identity op in cluster is pruned away.

// CHECK-LABEL: func @testIdentity
// CHECK-SAME: ([[ARG0:%.*]]: tensor<i32>)
func.func @testIdentity(%arg0: tensor<i32>) {
  // CHECK-NOT:  "tf.Identity"
  // CHECK:      "tf_device.cluster"
  // CHECK-NEXT: tf_device.return [[ARG0]]
  %0 = "tf_device.cluster"() ({
    %1 = "tf.Identity"(%arg0) : (tensor<i32>) -> tensor<i32>

  // CHECK-NEXT: bb0(%[[ARG_3:.*]]: tensor<i32>, %[[ARG_4:.*]]: tensor<*xf32>)
  ^bb0(%arg3: tensor<i32>, %arg4: tensor<*xf32>):
    // CHECK-NEXT: %[[RESULT_1:.*]] = "tf.Identity"(%[[ARG_3]])
    %1 = "tf.Identity"(%arg3) : (tensor<i32>) -> tensor<i32>
    %2 = "tf.Identity"(%arg1) : (tensor<i32>) -> tensor<i32>
    // CHECK-NEXT: %[[RESULT_2:.*]] = "tf.NotEqual"(%[[RESULT_1]], %[[ARG_1]])

# CHECK-SAME:  control_outputs = ""
# CHECK-SAME:  inputs = "input"
# CHECK-SAME:  outputs = "out:1,out"

# CHECK:           tf.Relu
# CHECK:           %[[IDENTITY:[a-z_0-9]+]]:2, {{.*}} = tf_executor.island wraps "tf.IdentityN"
# CHECK:           etch %[[IDENTITY]]#1, %[[IDENTITY]]#0

node {
  name: "input"
  op: "Placeholder"
  device: "/device:CPU:0"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }

    // CHECK:      tf.Identity
    // CHECK-SAME: device = "/job:localhost/replica:0/task:0/device:TPU:0"
    %1:2 = tf_executor.island wraps "tf.Identity"(%0#0) : (tensor<i64>) -> tensor<i64>
    // CHECK:      tf.IdentityN
    // CHECK-SAME: device = "/job:localhost/replica:0/task:0/device:TPU:0"

// CHECK: %[[conv:.*]] = "tf.Conv2D"(%[[ARG0]], %[[cast]])
// CHECK: %[[identity:.*]] = "tf.IdentityN"(%[[conv]]) {device = ""} : (tensor<1x3x2x2xf32>) -> tensor<1x3x2x2xf32>
// CHECK: return %[[identity]] : tensor<1x3x2x2xf32>

// -----

// Tests that `tf.BatchFunction` is inlined.

Harshavardhana <******@****.***> 1627436271 -0700

jiuker <******@****.***> 1684487613 +0800