tf.bitwise.left_shift

Elementwise computes the bitwise left-shift of x and y.

tf.bitwise.left_shift(
    x: _atypes.TensorFuzzingAnnotation[TV_LeftShift_T],
    y: _atypes.TensorFuzzingAnnotation[TV_LeftShift_T],
    name=None
) -> _atypes.TensorFuzzingAnnotation[TV_LeftShift_T]

If y is negative, or greater than or equal to the width of x in bits the result is implementation defined.

Example:

import tensorflow as tf
from tensorflow.python.ops import bitwise_ops
import numpy as np
dtype_list = [tf.int8, tf.int16, tf.int32, tf.int64]

for dtype in dtype_list:
  lhs = tf.constant([-1, -5, -3, -14], dtype=dtype)
  rhs = tf.constant([5, 0, 7, 11], dtype=dtype)

  left_shift_result = bitwise_ops.left_shift(lhs, rhs)

  print(left_shift_result)

# This will print:
# tf.Tensor([ -32   -5 -128    0], shape=(4,), dtype=int8)
# tf.Tensor([   -32     -5   -384 -28672], shape=(4,), dtype=int16)
# tf.Tensor([   -32     -5   -384 -28672], shape=(4,), dtype=int32)
# tf.Tensor([   -32     -5   -384 -28672], shape=(4,), dtype=int64)

lhs = np.array([-2, 64, 101, 32], dtype=np.int8)
rhs = np.array([-1, -5, -3, -14], dtype=np.int8)
bitwise_ops.left_shift(lhs, rhs)
# <tf.Tensor: shape=(4,), dtype=int8, numpy=array([ -2,  64, 101,  32], dtype=int8)>

x A Tensor. Must be one of the following types: int8, int16, int32, int64, uint8, uint16, uint32, uint64.
y A Tensor. Must have the same type as x.
name A name for the operation (optional).

A Tensor. Has the same type as x.