// an Attr with that value.
func Int(key string, value int) Attr {
	return Int64(key, int64(value))
}

// Uint64 returns an Attr for a uint64.
func Uint64(key string, v uint64) Attr {
	return Attr{key, Uint64Value(v)}
}

// Float64 returns an Attr for a floating-point number.
func Float64(key string, v float64) Attr {
	return Attr{key, Float64Value(v)}
}

// Bool returns an Attr for a bool.

node {
  name: "save/SaveV2/shape_and_slices"
  op: "Const"
  attr {
    key: "_output_shapes"
    value {
      list {
        shape {
          dim {
            size: 3
          }
        }
      }
    }
  }
  attr {
    key: "dtype"
    value {
      type: DT_STRING
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_STRING

Russ Cox <******@****.***> 1632321992 -0400

      name: "custom_relu"
    }
    attr {
      key: "_implements"
      value {
        func {
          name: "tensorflow.relu"
        }
      }
    }
  }
  function {
    signature {
      name: "custom_embedding_matmul"
    }
    attr {
      key: "_implements"
      value {
        func {
          name: "tensorflow.embedding_matmul"
          attr {
            key: "key1"

Adrian Kuegel <******@****.***> 1648469008 -0700

node {
  name: "_tf.PartitionedCall"
  op: "PartitionedCall"
  attr {
    key: "Tin"
    value {
      list {
      }
    }
  }
  attr {
    key: "Tout"
    value {
      list {
        type: DT_FLOAT
      }
    }
  }
  attr {
    key: "f"
    value {
      func {
        name: "foo"
      }
    }
  }
}
library {

  op: "Conv2D"
  input: "input"
  input: "MobilenetV1/Conv2d_0/weights/read"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "padding"
    value {
      s: "SAME"
    }
  }
  attr {
    key: "strides"
    value {
      list {
        i: 1
        i: 2
        i: 2
        i: 1
      }
    }
  }
}
node {

  }
}
node {
  name: "Case"
  op: "Case"
  input: "predicate"
  attr {
    key: "Tin"
    value {
      list {
      }
    }
  }
  attr {
    key: "Tout"
    value {
      list {
        type: DT_FLOAT
      }
    }
  }
  attr {
    key: "branches"
    value {
      list {
        func {
          name: "foo"
        }
        func {
          name: "bar"

// CHECK:        name: "output0"
// CHECK-NEXT:   op: "_Retval"
// CHECK-NEXT:   input: "input0"
// CHECK-NEXT:   attr {
// CHECK-NEXT:     key: "T"
// CHECK-NEXT:     value {
// CHECK-NEXT:       type: DT_INT32
// CHECK-NEXT:     }
// CHECK-NEXT:   }
// CHECK-NEXT:   attr {
// CHECK-NEXT:     key: "index"
// CHECK-NEXT:     value {
// CHECK-NEXT:       i: 0
// CHECK-NEXT:     }
// CHECK-NEXT:   }

# RUN: tf-mlir-translate -graphdef-to-mlir -tf-enable-shape-inference-on-import=false %s -o - | FileCheck %s

node {
  name: "input0"
  op: "Placeholder"
  attr {
    key: "dtype"
    value {
      type: DT_INT32
    }
  }
}
versions {
  producer: 29
  min_consumer: 12
}

# Verify that functions from the library are properly imported.

# CHECK-LABEL:  func @main() {
# CHECK:    "tf.Placeholder"