// CHECK: device = "gpu"
  %1 = "tf.MatMul"(%arg0, %0) {T = f32, _output_shapes = ["tfshape$dim { size: 3 } dim { size: 3 }"], device = "", transpose_a = false, transpose_b = false} : (tensor<3x1xf32>, tensor<1x3xf32>) -> tensor<3x3xf32>
  // CHECK: device = "cpu"
  %2 = "tf.Relu"(%1) {T = f32, _output_shapes = ["tfshape$dim { size: 3 } dim { size: 3 }"], device = "cpu"} : (tensor<3x3xf32>) -> tensor<3x3xf32>
  // CHECK: device = "gpu"

		fmt.Printf("\n")
	}
	// Output:
	// json.Delim: { (more)
	// string: Message (more)
	// string: Hello (more)
	// string: Array (more)
	// json.Delim: [ (more)
	// float64: 1 (more)
	// float64: 2 (more)
	// float64: 3
	// json.Delim: ] (more)
	// string: Null (more)
	// <nil>: <nil> (more)
	// string: Number (more)
	// float64: 1.234
	// json.Delim: }
}

      }
    }
  }
  attr {
    key: "_handle_shapes"
    value {
      list {
        shape {
          dim {
            size: 3
          }
          dim {
            size: 3
          }
          dim {
            size: 1
          }
          dim {
            size: 32
          }
        }
      }
    }
  }
  attr {
    key: "index"
    value {
      i: 1

  SmallVector<int64_t, 4> permutation_array(updates_type.getRank());
  int64_t dim = 0;
  // Move leading indices to the back of the array.
  const auto permutation_array_size = permutation_array.size();
  for (int64_t i = update_window_dims.size(); i < permutation_array_size; ++i) {
    permutation_array[i] = dim;
    ++dim;
  }
  // Move trailing indices to the front of the array.

  op: "Placeholder"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "shape"
    value {
      shape {
        dim {
          size: 2
        }
        dim {
          size: 5
        }
        dim {
          size: 3
        }
      }
    }
  }
}
node {
  name: "Placeholder_1"
  op: "Placeholder"
  attr {
    key: "dtype"
    value {

        dtype: DT_FLOAT
        tensor_shape {
          dim {
          }
        }
      }
    }
  }
}
node {
  name: "ParseExample/Const_1"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_FLOAT
        tensor_shape {
          dim {
          }
        }
      }
    }
  }

      tensor {
        dtype: DT_INT64
        tensor_shape {
          dim {
            size: 2
          }
        }
        tensor_content: "\x00\x00\x00\x00\x00\x00\x00\x00\x08\x00\x00\x00\x00\x00\x00\x00"
      }
    }
  }
  attr {
    key: "_output_shapes"
    value {
      list {
        shape {
          dim {
            size: 2
          }
        }
      }
    }
  }

  llvm::SmallVector<bool, 4> is_reduce_dim(rank, false);
  for (const APInt &index : indices.getValues<APInt>()) {
    int64_t dim = GetDimForAxis(index.getSExtValue(), rank);
    // Invalid input.
    if (dim < 0 || dim >= rank) return UnrankedTensorType::get(element_ty);

    if (!is_reduce_dim[dim]) {
      is_reduce_dim[dim] = true;
      num_reduce_dim++;
    }
  }

  ArrayRef<int64_t> shape = ranked_ty.getShape();

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

// CHECK-NEXT:   value {
// CHECK-NEXT:     list {
// CHECK-NEXT:       shape {
// CHECK-NEXT:         dim {
// CHECK-NEXT:           size: 3
// CHECK:            shape {
// CHECK-NEXT:         dim {
// CHECK-NEXT:           size: 4
// CHECK-NEXT:         }
// CHECK-NEXT:         dim {
// CHECK-NEXT:           size: -1
// CHECK:            shape {
// CHECK-NEXT:         unknown_rank: true