Function zeros_like_v2

tensorflow/python/ops/array_ops.py:2387–2423

Creates a tensor with all elements set to zero. Given a single tensor (`tensor`), this operation returns a tensor of the same type and shape as `tensor` with all elements set to zero. Optionally, you can use `dtype` to specify a new type for the returned tensor. For example: ```python

(
    input,  # pylint: disable=redefined-builtin
    dtype=None,
    name=None)

@tf_export("zeros_like", v1=[])
@dispatch.add_dispatch_support
def zeros_like_v2(
    input,  # pylint: disable=redefined-builtin
    dtype=None,
    name=None):
  """Creates a tensor with all elements set to zero.

  Given a single tensor (`tensor`), this operation returns a tensor of the
  same type and shape as `tensor` with all elements set to zero. Optionally,
  you can use `dtype` to specify a new type for the returned tensor.

  For example:

  ```python
  tensor = tf.constant([[1, 2, 3], [4, 5, 6]])
  tf.zeros_like(tensor)  # [[0, 0, 0], [0, 0, 0]] with dtype=int32

  If dtype of input `tensor` is `float32`, then the output is also of `float32`
  tensor = tf.constant([[1.0, 2.0, 3.0], [4, 5, 6]])
  tf.zeros_like(tensor)  # [[0., 0., 0.], [0., 0., 0.]] with dtype=floa32

  If you want to specify desired dtype of output `tensor`, then specify it in
  the op tensor = tf.constant([[1.0, 2.0, 3.0], [4, 5, 6]])
  tf.zeros_like(tensor,dtype=tf.int32)  # [[0, 0, 0], [0, 0, 0]] with
  dtype=int32
  ```

  Args:
    input: A `Tensor`.
    dtype: A type for the returned `Tensor`. Must be `float16`, `float32`,
      `float64`, `int8`, `uint8`, `int16`, `uint16`, `int32`, `int64`,
      `complex64`, `complex128`, `bool` or `string`.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` with all elements set to zero.
  """
  return zeros_like_impl(input, dtype, name, optimize=True)


def zeros_like_impl(tensor, dtype, name, optimize=True):