op    : "Add"
  input : "x_const"
  input : "y_const"
  attr { key  : "T" value { type: DT_INT32 } }
}
node {
  name  : "z"
  op    : "SomeUnknownOp"
  input : "x_const"
}
node {
  name  : "z_identity"
  op    : "Identity"
  input : "z:1"
  attr { key  : "T" value { type: DT_INT32 } }
}
node {
  name  : "x_z_sum"
  op    : "Add"
  input : "x_const"
  input : "z_identity"

node {
  name  : "x_const"
  op    : "Const"
  attr {
    key: "value"
    value {
      tensor { dtype: DT_INT32 tensor_shape { dim { size: 1 } } int_val: 1 }
    }
  }
  attr {
    key  : "dtype"
    value {
      type       : DT_INT32
    }
  }
}
node {
  name  : "y_const"
  op    : "Const"
  attr {
    key: "value"
    value {
      tensor { dtype: DT_INT32 tensor_shape { dim { size: 1 } } int_val: 2 }

# Text form of tensorflow.tf2xla.Config proto.
feed {
  id { node_name: "x_const" }
  shape {
    dim { size: 1 }
  }
}
feed {
  id { node_name: "y_const" }
  shape {
    dim { size: 1 }
  }
}
feed {
  id { node_name: "z_identity"}
  shape {
    dim { size: 1 }
  }
}
fetch {
  id { node_name: "x_y_sum" }
}
fetch {
  id { node_name: "x_z_sum" }

# Text form of tensorflow.tf2xla.Config proto.
feed {
  id { node_name: "x_const" }
  shape {
    dim { size: 1 }
  }
}
feed {
  id { node_name: "y_const" }
  shape {
    dim { size: 1 }
  }
}
fetch {
  id { node_name: "func_call" }

# Text form of tensorflow.tf2xla.Config proto.
feed {
  id { node_name: "x_const" }
  shape {
    dim { size: 1 }
  }
}
feed {
  id { node_name: "y_const" }
  shape {
    dim { size: 1 }
  }
}
fetch {
  id { node_name: "x_y_sum" }

# Text form of tensorflow.tf2xla.Config proto.
feed {
  id { node_name: "x_const" }
  shape {
    dim { size: 1 }
  }
}
feed {
  id { node_name: "y_const" }
  shape {
    dim { size: 1 }
  }
}
feed {
  id { node_name: "z" output_index: 1}
  shape {
    dim { size: 1 }
  }
  type: DT_INT32
}
fetch {
  id { node_name: "x_y_sum" }
}
fetch {
  id { node_name: "x_z_sum" }

# Text form of tensorflow.tf2xla.Config proto.
feed {
  id { node_name: "x_const" }
  shape {
    dim { size: 1 }
  }
}
feed {
  id { node_name: "y_const" }
  shape {
    dim { size: 1 }
  }
}
fetch {
  id { node_name: "x_y_sum" }

  auto x = Variable(scope_test_, {2, 3}, DT_DOUBLE);
  auto x_const = Const(scope_test_, {{1.0, 2.0, 3.0}, {4.0, 5.0, 6.0}});
  auto x_assign = Assign(scope_test_, x, x_const);

  auto y = Variable(scope_test_, {3, 1}, DT_DOUBLE);
  auto y_const = Const(scope_test_, {{1.0}, {2.0}, {3.0}});
  auto y_assign = Assign(scope_test_, y, y_const);

  auto m = MatMul(scope_test_, x, y);

from tensorflow.python.ops import variables
from tensorflow.python.training import saver as saver_lib

FLAGS = None


def tfadd(_):
  x = constant_op.constant([1], name='x_const')
  y = constant_op.constant([2], name='y_const')
  math_ops.add(x, y, name='x_y_sum')


def tfadd_with_ckpt(out_dir):
  x = array_ops.placeholder(dtypes.int32, name='x_hold')

package main

import "os"

const (
	c0      = 0
	cm1     = -1
	chuge   = 1 << 100
	chuge_1 = chuge - 1
	c1      = chuge >> 100
	c3div2  = 3 / 2
	c1e3    = 1e3

	rsh1 = 1e100 >> 1000
	rsh2 = 1e302 >> 1000

	ctrue  = true
	cfalse = !ctrue

	// Issue #34563
	_ = string(int(123))
	_ = string(rune(456))
)

const (
	f0              = 0.0
	fm1             = -1.