ERROR_JOIN_TO_SUBST                                                       syscall.Errno = 140
	ERROR_SUBST_TO_JOIN                                                       syscall.Errno = 141
	ERROR_BUSY_DRIVE                                                          syscall.Errno = 142
	ERROR_SAME_DRIVE                                                          syscall.Errno = 143

loseParenthesis=D.caret=D.bang=D.backslash=D.at=D.asterisk=D.ampersand=void 0;var q1=38;D.ampersand=q1;var R1=42;D.asterisk=R1;var M1=64;D.at=M1;var B1=44;D.comma=B1;var F1=58;D.colon=F1;var L1=59;D.semicolon=L1;var N1=40;D.openParenthesis=N1;var $1=41;D.closeParenthesis=$1;var j1=91;D.openSquare=j1;var z1=93;D.closeSquare=z1;var V1=36;D.dollar=V1;var U1=126;D.tilde=U1;var W1=94;D.caret=W1;var G1=43;D.plus=G1;var H1=61;D.equals=H1;var Y1=124;D.pipe=Y1;var Q1=62;D.greaterThan=Q1;var J1=32;D.space=J1;var...

merged result. If you do not need this guarantee, ParallelDynamicStitch might
perform better on some devices.

For example:

```python
    indices[0] = 6
    indices[1] = [4, 1]
    indices[2] = [[5, 2], [0, 3]]
    data[0] = [61, 62]
    data[1] = [[41, 42], [11, 12]]
    data[2] = [[[51, 52], [21, 22]], [[1, 2], [31, 32]]]
    merged = [[1, 2], [11, 12], [21, 22], [31, 32], [41, 42],

  BuiltinOperator_HASHTABLE_FIND = 137,
  BuiltinOperator_HASHTABLE_IMPORT = 138,
  BuiltinOperator_HASHTABLE_SIZE = 139,
  BuiltinOperator_REDUCE_ALL = 140,
  BuiltinOperator_CONV_3D_TRANSPOSE = 141,
  BuiltinOperator_VAR_HANDLE = 142,
  BuiltinOperator_READ_VARIABLE = 143,
  BuiltinOperator_ASSIGN_VARIABLE = 144,
  BuiltinOperator_BROADCAST_ARGS = 145,
  BuiltinOperator_RANDOM_STANDARD_NORMAL = 146,

Tang, Zhengsheng Wei, zxcqwe4906, 张志豪, 田传武

We are also grateful to all who filed issues or helped resolve them, asked and
answered questions, and were part of inspiring discussions.

# Release 1.4.1

## Bug Fixes and Other Changes

*   `LinearClassifier` fix.

# Release 1.4.0

## Major Features And Improvements

*   `tf.keras` is now part of the core TensorFlow API.

      list {
        shape {
          dim {
            size: 1024
          }
        }
      }
    }
  }
  attr {
    key: "index"
    value {
      i: 141
    }
  }
}
node {
  name: "bn4a_branch1_cond_input_3"
  op: "_Arg"
  device: "/job:localhost/replica:0/task:0/device:GPU:0"
  attr {
    key: "T"
    value {

func (m *PodPortForwardOptions) Reset()      { *m = PodPortForwardOptions{} }
func (*PodPortForwardOptions) ProtoMessage() {}
func (*PodPortForwardOptions) Descriptor() ([]byte, []int) {
	return fileDescriptor_6c07b07c062484ab, []int{141}
}
func (m *PodPortForwardOptions) XXX_Unmarshal(b []byte) error {
	return m.Unmarshal(b)
}
func (m *PodPortForwardOptions) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
	b = b[:cap(b)]