@tf_export('math.truediv', 'truediv')
@dispatch.add_dispatch_support
def truediv(x, y, name=None):
    'Divides x / y elementwise (using Python 3 division operator semantics).\n\n  NOTE: Prefer using the Tensor operator or tf.divide which obey Python\n  division operator semantics.\n\n  This function forces Python 3 division operator semantics where all integer\n  arguments are cast to floating types first.   This op is generated by normal\n  `x / y` division in Python 3 and in Python 2.7 with\n  `from __future__ import division`.  If you want integer division that rounds\n  down, use `x // y` or `tf.math.floordiv`.\n\n  `x` and `y` must have the same numeric type.  If the inputs are floating\n  point, the output will have the same type.  If the inputs are integral, the\n  inputs are cast to `float32` for `int8` and `int16` and `float64` for `int32`\n  and `int64` (matching the behavior of Numpy).\n\n  Args:\n    x: `Tensor` numerator of numeric type.\n    y: `Tensor` denominator of numeric type.\n    name: A name for the operation (optional).\n\n  Returns:\n    `x / y` evaluated in floating point.\n\n  Raises:\n    TypeError: If `x` and `y` have different dtypes.\n  '
    return _truediv_python3(x, y, name)
Divides x / y elementwise (using Python 3 division operator semantics).

NOTE: Prefer using the Tensor operator or tf.divide which obey Python
division operator semantics.

This function forces Python 3 division operator semantics where all integer
arguments are cast to floating types first.   This op is generated by normal
`x / y` division in Python 3 and in Python 2.7 with
`from __future__ import division`.  If you want integer division that rounds
down, use `x // y` or `tf.math.floordiv`.

`x` and `y` must have the same numeric type.  If the inputs are floating
point, the output will have the same type.  If the inputs are integral, the
inputs are cast to `float32` for `int8` and `int16` and `float64` for `int32`
and `int64` (matching the behavior of Numpy).

Args:
  x: `Tensor` numerator of numeric type.
  y: `Tensor` denominator of numeric type.
  name: A name for the operation (optional).

Returns:
  `x / y` evaluated in floating point.

Raises:
  TypeError: If `x` and `y` have different dtypes.
@tf_export('math.truediv', 'truediv')
@dispatch.add_dispatch_support
def truediv(x, y, name=None):
    'Divides x / y elementwise (using Python 3 division operator semantics).\n\n  NOTE: Prefer using the Tensor operator or tf.divide which obey Python\n  division operator semantics.\n\n  This function forces Python 3 division operator semantics where all integer\n  arguments are cast to floating types first.   This op is generated by normal\n  `x / y` division in Python 3 and in Python 2.7 with\n  `from __future__ import division`.  If you want integer division that rounds\n  down, use `x // y` or `tf.math.floordiv`.\n\n  `x` and `y` must have the same numeric type.  If the inputs are floating\n  point, the output will have the same type.  If the inputs are integral, the\n  inputs are cast to `float32` for `int8` and `int16` and `float64` for `int32`\n  and `int64` (matching the behavior of Numpy).\n\n  Args:\n    x: `Tensor` numerator of numeric type.\n    y: `Tensor` denominator of numeric type.\n    name: A name for the operation (optional).\n\n  Returns:\n    `x / y` evaluated in floating point.\n\n  Raises:\n    TypeError: If `x` and `y` have different dtypes.\n  '
    return _truediv_python3(x, y, name)