package cmd

// SetupType - enum for setup type.
type SetupType int

const (
	// UnknownSetupType - starts with unknown setup type.
	UnknownSetupType SetupType = iota

	// FSSetupType - FS setup type enum.
	FSSetupType

	// ErasureSDSetupType - Erasure single drive setup enum.
	ErasureSDSetupType

	// ErasureSetupType - Erasure setup type enum.
	ErasureSetupType

  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_INT32
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_INT32
        tensor_shape {
        }
        int_val: 3
      }
    }
  }
  experimental_debug_info {
  }
}
node {
  name: "TensorListSetItem/index"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_INT32

  ASSERT_TRUE(status.ok()) << status.message();

  TF_DataType dtype = TypeParam::kDType;
  // This is our 1D tensor of varying dtype.
  std::vector<typename TypeParam::type> value = {42, 100, 0, 1, 4, 29};
  // Shape is Rank 2 vector with shape 2 x 3.
  std::vector<int64_t> shape({2, 3});

  Tensor original_tensor = Tensor::FromBuffer(
      /*dtype=*/dtype, /*shape=*/shape,
      /*data=*/value.data(),

    %0:2 = tf_executor.island wraps "tf.Const"() {device = "", dtype = "tfdtype$DT_HALF", value = dense<1.0> : tensor<1x2xf16>} : () -> tensor<1x2xf16> loc("const1")
// CHECK: node {
// CHECK-NEXT: name: "const1"
// CHECK-NEXT: op: "Const"
// CHECK: dtype: DT_HALF
// CHECK: half_val: 15360

  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_STRING
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_STRING
        tensor_shape {
          dim {
            size: 1
          }
        }
        string_val: "value"
      }
    }
  }
}
node {
  name: "Const_1"
  op: "Const"
  attr {
    key: "dtype"
    value {

  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "shape"
    value {
      shape {
        dim {
          size: -1
        }
        dim {
          size: 1
        }
      }
    }
  }
}
node {
  name: "sequential/quant_dense/MatMul/ReadVariableOp/resource"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }

  // CHECK: tf.ReadVariableOp
  // CHECK-SAME: dtype = !corert.quint8
  %0 = "tf.ReadVariableOp"(%arg0) {_output_shapes = ["tfshape$dim { size: 1 } dim { size: 3 }"], device = "/device:CPU:0", dtype = !tf_type.quint8} : (tensor<!tf_type.resource<tensor<1x3x!tf_type.quint8>>>) -> tensor<1x3x!tf_type.quint8>

  // CHECK: tf.ReadVariableOp
  // CHECK-SAME: dtype = !corert.quint16

Rita Zhang <******@****.***> 1677726350 -0800

// RUN: tf-opt %s -split-input-file -tfl-reduce-type-precision -verify-diagnostics

func.func @testI8ToI4WithinRange() -> (tensor<4xi8>) {
  %0 = arith.constant dense<[-8, 0, 1, 7]> : tensor<4xi8>
  // expected-error@+1 {{type of return operand 0 ('tensor<4xi4>') doesn't match function result type ('tensor<4xi8>')}}
  func.return %0 : tensor<4xi8>
}

func.func @testI8ToI4NotWithinRange() -> tensor<4xi8> {
  %0 = arith.constant dense<[-10, 2, 3, 8]> : tensor<4xi8>

import kotlin.reflect.KClass
import kotlin.reflect.KType

internal fun SmartPrinter.printTypeWithShortNames(type: KType, shouldRenderFqName: (KType) -> Boolean = { false }) {
    fun typeConversion(type: KType): String {
        val nullableSuffix = if (type.isMarkedNullable) "?" else ""
        val simpleName = if (shouldRenderFqName(type)) {
            type.qualifiedName
        } else {
            type.simpleName
        }