# Graph Mode Syntax - Python Built-in Functions
[](https://gitee.com/mindspore/docs/blob/master/tutorials/source_en/compile/python_builtin_functions.md)
Python built-in functions supported by the current static graph mode include: `int`, `float`, `bool`, `str`, `tuple`, `list`, `dict`, `getattr`, `hasattr`, `len`, `isinstance`, `all`, `any`, `round`, `max`, `min`, `sum`, `abs`, `map`, `zip` 、`range`、`enumerate`、`super`、`pow`、`print`、`filter`、`type`。 The use of built-in functions in graph mode is similar to the corresponding Python built-in functions.
## int
Function: Return the integer value based on the input number or string.
Call: `int(x=0, base=10)`, converted to decimal by default.
Input parameter:
- `x` - the object need to be converted to integer, the valid type of x includes `int`, `float`, `bool`, `str`, `Tensor` and third-party object (such as `numpy.ndarray`).
- `base` - the base to convert. `base` is only allowed when `x` is constant `str`.
Return value: the converted integer.
For example:
```python
import mindspore as ms
@ms.jit
def func(x):
a = int(3)
b = int(3.6)
c = int('12', 16)
d = int('0xa', 16)
e = int('10', 8)
f = int(x)
return a, b, c, d, e, f
x = ms.Tensor([-1.0], ms.float32)
a, b, c, d, e, f = func(x)
print("a: ", a)
print("b: ", b)
print("c: ", c)
print("d: ", d)
print("e: ", e)
print("f: ", f)
```
The result is as follows:
```text
a: 3
b: 3
c: 18
d: 10
e: 8
f: -1
```
## float
Function: Return the floating-point number based on the input number or string.
Calling: `float(x=0)`.
Input parameter: `x` - the object need to be converted to floating number, the valid type of x includes `int`, `float`, `bool`, `str`, `Tensor` and third-party object (such as `numpy.ndarray`).
Return value: the converted floating-point number.
For example:
```python
import mindspore as ms
@ms.jit
def func(x):
a = float(1)
b = float(112)
c = float(-123.6)
d = float('123')
e = float(x.asnumpy())
return a, b, c, d, e
x = ms.Tensor([-1], ms.int32)
a, b, c, d, e = func(x)
print("a: ", a)
print("b: ", b)
print("c: ", c)
print("d: ", d)
print("e: ", e)
```
The result is as follows:
```text
a: 1.0
b: 112.0
c: -123.5999984741211
d: 123.0
e: -1.0
```
## bool
Function: Return the boolean value based on the input.
Calling: `bool(x=false)`
Input parameter: `x` - the object need to be converted to boolean value, the valid type of x includes `int`, `float`, `bool`, `str`, `list`, `tuple`, `dict`, `Tensor` and third-party object (such as `numpy.ndarray`).
Return value: the converted boolean scalar.
For example:
```python
import mindspore as ms
@ms.jit
def func():
a = bool()
b = bool(0)
c = bool("abc")
d = bool([1, 2, 3, 4])
e = bool(ms.Tensor([10]).asnumpy())
return a, b, c, d, e
a, b, c, d, e = func()
print("a: ", a)
print("b: ", b)
print("c: ", c)
print("d: ", d)
print("e: ", e)
```
The result is as follows:
```text
a: False
b: False
c: True
d: True
e: True
```
## str
Function: Return the string value based on the input.
Calling: `str(x='')`
Input parameter: `x` - the object need to be converted to string value, the valid type of x includes `int`, `float`, `bool`, `str`, `list`, `tuple`, `dict`, `Tensor` and third-party object (such as `numpy.ndarray`).
Return value: string converted from `x`.
For example, a is an empty string:
```python
import numpy as np
import mindspore as ms
@ms.jit
def func():
a = str()
b = str(0)
c = str([1, 2, 3, 4])
d = str(ms.Tensor([10]))
e = str(np.array([1, 2, 3, 4]))
return a, b, c, d, e
a, b, c, d, e = func()
print("a: ", a)
print("b: ", b)
print("c: ", c)
print("d: ", d)
print("e: ", e)
```
The result is as follows:
```text
a: # a is empty string
b: 0
c: [1, 2, 3, 4]
d: Tensor(shape=[1], dtype=Int64, value=[10])
e: [1 2 3 4]
```
## tuple
Function: Return a tuple based on the input object.
Calling: `tuple(x=())`.
Input parameter: `x` - the object that need to be converted to tuple, the valid type of x includes `list`, `tuple`, `dict`, `Tensor` or third-party object (such as `numpy.ndarray`).
Return value: tuple with elements of `x`, `x` is cut based on zero dimension.
For example:
```python
import numpy as np
import mindspore as ms
@ms.jit
def func():
a = tuple((1, 2, 3))
b = tuple(np.array([1, 2, 3]))
c = tuple({'a': 1, 'b': 2, 'c': 3})
d = tuple(ms.Tensor([1, 2, 3]))
return a, b, c, d
a, b, c, d = func()
print("a: ", a)
print("b: ", b)
print("c: ", c)
print("d: ", d)
```
The result is as follows:
```text
a: (1, 2, 3)
b: (1, 2, 3)
c: ('a', 'b', 'c')
d: (Tensor(shape=[], dtype=Int64, value= 1), Tensor(shape=[], dtype=Int64, value= 2), Tensor(shape=[], dtype=Int64, value= 3))
```
## list
Function: Return a list based on the input object.
Calling: `list(x=())`.
Input parameter: `x` - the object that need to be converted to list, the valid type of x includes `list`, `tuple`, `dict`, `Tensor` or third-party object (such as `numpy.ndarray`).
Return value: list with elements of `x`, `x` is cut based on zero dimension.
For example:
```python
import numpy as np
import mindspore as ms
@ms.jit
def func():
a = list((1, 2, 3))
b = list(np.array([1, 2, 3]))
c = list({'a':1, 'b':2, 'c':3})
d = list(ms.Tensor([1, 2, 3]))
return a, b, c, d
a_t, b_t, c_t, d_t = func()
print("a_t: ", a_t)
print("b_t: ", b_t)
print("c_t: ", c_t)
print("d_t: ", d_t)
```
The result is as follows:
```text
a_t: [1, 2, 3]
b_t: [1, 2, 3]
c_t: ['a', 'b', 'c']
d_t: [Tensor(shape=[], dtype=Int64, value= 1), Tensor(shape=[], dtype=Int64, value= 2), Tensor(shape=[], dtype=Int64, value= 3)]
```
## dict
Function: Used to create a dictionary.
Examples of code usage are as follows:
```python
import mindspore as ms
@ms.jit
def func():
a = dict() # Create an empty dictionary
b = dict(a='a', b='b', t='t') # Pass in keywords
c = dict(zip(['one', 'two', 'three'], [1, 2, 3])) # Mapping function approach to constructing dictionaries
d = dict([('one', 1), ('two', 2), ('three', 3)]) # Iterable object approach to constructing dictionaries
return a, b, c, d
a, b, c, d = func()
print("a: ", a)
print("b: ", b)
print("c: ", c)
print("d: ", d)
```
```text
a: {}
b: {'a': 'a', 'b': 'b', 't': 't'}
c: {'one': 1, 'two': 2, 'three': 3}
d: {'one': 1, 'two': 2, 'three': 3}
```
## getattr
Function: Get the attribute of python object.
Calling: `getattr(x, attr, default)`.
Input parameter:
- `x` - The object to get attribute, `x` can be all types that graph mode supports. Third-party library types are also supported when the JIT syntax support level option is 'Lax'.
- `attr` - The name of the attribute, the type of `attr` should be `str`.
- `default` - Optional input. If `x` do not have `attr`, `default` will be returned. `default` can be all types that graph mode supports. Third-party library types are also supported when the JIT syntax support level option is 'Lax'. If `default` is not set and `x` does not have attribute `attr`, AttributeError will be raised.
Return value: Target attribute or `default`.
For example:
```python
import numpy as np
import mindspore as ms
@ms.jit_class
class MSClass1:
def __init__(self):
self.num0 = 0
ms_obj = MSClass1()
@ms.jit
def func(x):
a = getattr(ms_obj, 'num0')
b = getattr(ms_obj, 'num1', 2)
c = getattr(x.asnumpy(), "shape", np.array([0, 1, 2, 3, 4]))
return a, b, c
x = ms.Tensor([-1.0], ms.float32)
a, b, c = func(x)
print("a: ", a)
print("b: ", b)
print("c: ", c)
```
The result is as follows:
```text
a: 0
b: 2
c: (1,)
```
The attribute of object in graph mode may be different from that in pynative mode. It is suggested to use `default` input or call `hasattr` before using `getattr` to avoid AttributeError.
'getattr(x.asnumpy(), "shape", np.array([0, 1, 2, 3, 4]))' is a high-level usage, and more introduction can be found in the [AST Extended Syntaxes (LAX level)](https://www.mindspore.cn/tutorials/en/master/compile/static_graph.html#ast-extended-syntaxes-lax-level) chapter.
## hasattr
Function: Judge whether an object has an attribute.
Calling: `hasattr(x, attr)`.
Input parameter:
- `x` - The object to get attribute, `x` can be all types that graph mode supports. Third-party library types are also supported when the JIT syntax support level option is 'Lax'.
- `attr` - The name of the attribute, the type of `attr` should be `str`.
Return value: boolean value indicates whether `x` has `attr`.
For example:
```python
import numpy as np
import mindspore as ms
from mindspore import Tensor
@ms.jit_class
class MSClass1:
def __init__(self):
self.num0 = 0
ms_obj = MSClass1()
@ms.jit
def func():
a = hasattr(ms_obj, 'num0')
b = hasattr(ms_obj, 'num1')
c = hasattr(Tensor(np.array([1, 2, 3, 4])).asnumpy(), "__len__")
return a, b, c
a, b, c = func()
print("a: ", a)
print("b: ", b)
print("c: ", c)
```
The result is as follows:
```text
a: True
b: False
c: True
```
'hasattr(Tensor(np.array([1, 2, 3, 4])).asnumpy(), "__len__")' is a high-level usage, and more introduction can be found in the [AST Extended Syntaxes (LAX level)](https://www.mindspore.cn/tutorials/en/master/compile/static_graph.html#ast-extended-syntaxes-lax-level) chapter.
## len
Function: Return the length of an object (string or other iterable object).
Calling: `len(sequence)`.
Input parameter: `sequence` - `Tuple`, `List`, `Dictionary`, `Tensor` or third-party object (such as numpy.ndarray).
Return value: length of the sequence, which is of the `int` type. If the input parameter is `Tensor`, the length of dimension 0 is returned.
For example:
```python
import numpy as np
import mindspore as ms
z = ms.Tensor(np.ones((6, 4, 5)))
@ms.jit()
def test(w):
x = (2, 3, 4)
y = [2, 3, 4]
d = {"a": 2, "b": 3}
n = np.array([1, 2, 3, 4])
x_len = len(x)
y_len = len(y)
d_len = len(d)
z_len = len(z)
n_len = len(n)
w_len = len(w.asnumpy())
return x_len, y_len, d_len, z_len, n_len, w_len
input_x = ms.Tensor([1, 2, 3, 4])
x_len, y_len, d_len, z_len, n_len, w_len = test(input_x)
print('x_len:{}'.format(x_len))
print('y_len:{}'.format(y_len))
print('d_len:{}'.format(d_len))
print('z_len:{}'.format(z_len))
print('n_len:{}'.format(n_len))
print('w_len:{}'.format(w_len))
```
The result is as follows:
```text
x_len:3
y_len:3
d_len:2
z_len:6
n_len:4
w_len:4
```
'len(w.asnumpy())' is a high-level usage, and more introduction can be found in the [AST Extended Syntaxes (LAX level)](https://www.mindspore.cn/tutorials/en/master/compile/static_graph.html#ast-extended-syntaxes-lax-level) chapter.
## isinstance
Function: Determines whether an object is an instance of a class.
Calling: `isinstance(obj, type)`.
Input parameters:
- `obj` - Any instance of any supported type.
- `type` - `bool`, `int`, `float`, `str`, `list`, `tuple`, `dict`, `Tensor`, `Parameter`, or the types of third-party libraries (e.g. numpy.ndarray) or a `tuple` containing only those types.
Return value: If `obj` is an instance of `type`, return `True`. Otherwise, return `False`.
For example:
```python
import mindspore as ms
import numpy as np
z = ms.Tensor(np.ones((6, 4, 5)))
@ms.jit()
def test(w):
x = (2, 3, 4)
y = [2, 3, 4]
x_is_tuple = isinstance(x, tuple)
y_is_list = isinstance(y, list)
z_is_tensor = isinstance(z, ms.Tensor)
w_is_ndarray = isinstance(w.asnumpy(), np.ndarray)
return x_is_tuple, y_is_list, z_is_tensor, w_is_ndarray
w = ms.Tensor(np.array([-1, 2, 4]))
x_is_tuple, y_is_list, z_is_tensor, w_is_ndarray = test(w)
print('x_is_tuple:{}'.format(x_is_tuple))
print('y_is_list:{}'.format(y_is_list))
print('z_is_tensor:{}'.format(z_is_tensor))
print('w_is_ndarray:{}'.format(w_is_ndarray))
```
The result is as follows:
```text
x_is_tuple:True
y_is_list:True
z_is_tensor:True
w_is_ndarray:True
```
'isinstance(w.asnumpy(), np.ndarray)' is a high-level usage, and more introduction can be found in the [AST Extended Syntaxes (LAX level)](https://www.mindspore.cn/tutorials/en/master/compile/static_graph.html#ast-extended-syntaxes-lax-level) chapter.
## all
Function: Judge whether all of the elements in the input is true.
Calling: `all(x)`.
Input parameter: - `x` - Iterable object, the valid types include `list`, `tuple`, `dict`, `Tensor` and third-party object (such as `numpy.ndarray`).
Return value: boolean, return `True` if all elements are `True`, otherwise `False`.
For example:
```python
import numpy as np
import mindspore as ms
from mindspore import Tensor
@ms.jit
def func():
a = all(['a', 'b', 'c', 'd'])
b = all(['a', 'b', '', 'd'])
c = all([0, 1, 2, 3])
d = all(('a', 'b', 'c', 'd'))
e = all(('a', 'b', '', 'd'))
f = all((0, 1, 2, 3))
g = all([])
h = all(())
x = Tensor(np.array([0, 1, 2, 3]))
i = all(x.asnumpy())
return a, b, c, d, e, f, g, h, i
a, b, c, d, e, f, g, h, i = func()
print("a: ", a)
print("b: ", b)
print("c: ", c)
print("d: ", d)
print("e: ", e)
print("f: ", f)
print("g: ", g)
print("h: ", h)
print("i: ", i)
```
The result is as follows:
```text
a: True
b: False
c: False
d: True
e: False
f: False
g: True
h: True
i: False
```
'all(x.asnumpy())' is a high-level usage, and more introduction can be found in the [AST Extended Syntaxes (LAX level)](https://www.mindspore.cn/tutorials/en/master/compile/static_graph.html#ast-extended-syntaxes-lax-level) chapter.
## any
Function: Judge whether any of the elements in the input is true.
Calling: `any(x)`.
Input parameter: - `x` - Iterable object, the valid types include `list`, `tuple`, `dict`, `Tensor` and third-party object (such as `numpy.ndarray`).
Return value: boolean, return `False` if all elements are `False`, otherwise `True`. Elements count as `True` except for 0, null, and `False`.
For example:
```python
import numpy as np
import mindspore as ms
from mindspore import Tensor
@ms.jit
def func():
a = any(['a', 'b', 'c', 'd'])
b = any(['a', 'b', '', 'd'])
c = any([0, '', False])
d = any(('a', 'b', 'c', 'd'))
e = any(('a', 'b', '', 'd'))
f = any((0, '', False))
g = any([])
h = any(())
x = Tensor(np.array([0, 1, 2, 3]))
i = any(x.asnumpy())
return a, b, c, d, e, f, g, h, i
a, b, c, d, e, f, g, h, i = func()
print("a: ", a)
print("b: ", b)
print("c: ", c)
print("d: ", d)
print("e: ", e)
print("f: ", f)
print("g: ", g)
print("h: ", h)
print("i: ", i)
```
The result is as follows:
```text
a: True
b: True
c: False
d: True
e: True
f: False
g: False
h: False
i: True
```
## round
Function: Return the rounding value of input.
Calling: `round(x, digit=0)`
Input parameter:
- `x` - the object to rounded, the valid types include `int`, `float`, `bool`, `Tensor` and third-party object that defines magic function `__round__()`.
- `digit` - the number of decimal places to round, the default value is 0. `digit` can be `int` object or `None`. If `x` is `Tensor`, then `round()` does not support input `digit`.
Return value: the value after rounding.
For example:
```python
import mindspore as ms
@ms.jit
def func():
a = round(10)
b = round(10.123)
c = round(10.567)
d = round(10, 0)
e = round(10.72, -1)
f = round(17.12, -1)
g = round(10.17, 1)
h = round(10.12, 1)
return a, b, c, d, e, f, g, h
a, b, c, d, e, f, g, h = func()
print("a: ", a)
print("b: ", b)
print("c: ", c)
print("d: ", d)
print("e: {:.2f}".format(e))
print("f: {:.2f}".format(f))
print("g: {:.2f}".format(g))
print("h: {:.2f}".format(h))
```
The result is as follows:
```text
a: 10
b: 10
c: 11
d: 10
e: 10.00
f: 20.00
g: 10.20
h: 10.10
```
## max
Function: Return the maximum of inputs.
Calling: `max(*data)`.
Input parameter: - `*data` - If `*data` is single input, `max` will compare all elements within `data` and `data` must be iterable object. If there are multiple inputs, then `max()` will compare each of them. The valid types of `data` include `int`, `float`, `bool`, `list`, `tuple`, `dict`, `Tensor` and third-party object (such as `numpy.ndarray`).
Return value: boolean, the maximum of the inputs.
For example:
```python
import numpy as np
import mindspore as ms
@ms.jit
def func():
a = max([0, 1, 2, 3])
b = max((0, 1, 2, 3))
c = max({1: 10, 2: 20, 3: 3})
d = max(np.array([1, 2, 3, 4]))
e = max(('a', 'b', 'c'))
f = max((1, 2, 3), (1, 4))
g = max(ms.Tensor([1, 2, 3]))
return a, b, c, ms.Tensor(d), e, f, g
a, b, c, d, e, f, g = func()
print("a: ", a)
print("b: ", b)
print("c: ", c)
print("d: ", d)
print("e: ", e)
print("f: ", f)
print("g: ", g)
```
The result is as follows:
```text
a: 3
b: 3
c: 3
d: 4
e: c
f: (1, 4)
g: 3
```
## min
Function: Return the minimum of inputs.
Calling: `min(*data)`.
Input parameter: - `*data` - If `*data` is single input, then `min()` will compare all elements within `data` and `data` must be iterable object. If there are multiple inputs, then `min()` will compare each of them. The valid types of `data` include `int`, `float`, `bool`, `list`, `tuple`, `dict`, `Tensor` and third-party object (such as `numpy.ndarray`).
Return value: boolean, the minimum of the inputs.
For example:
```python
import numpy as np
import mindspore as ms
@ms.jit
def func():
a = min([0, 1, 2, 3])
b = min((0, 1, 2, 3))
c = min({1: 10, 2: 20, 3: 3})
d = min(np.array([1, 2, 3, 4]))
e = min(('a', 'b', 'c'))
f = min((1, 2, 3), (1, 4))
g = min(ms.Tensor([1, 2, 3]))
return a, b, c, ms.Tensor(d), e, f, g
a, b, c, d, e, f, g = func()
print("a: ", a)
print("b: ", b)
print("c: ", c)
print("d: ", d)
print("e: ", e)
print("f: ", f)
print("g: ", g)
```
The result is as follows:
```text
a: 0
b: 0
c: 1
d: 1
e: a
f: (1, 2, 3)
g: 1
```
## sum
Function: Return the sum of input sequence.
Calling: `sum(x, n=0)`.
Input parameter:
- `x` - iterable with numbers, the valid types include `list`, `tuple`, `Tensor` and third-party object (such as `numpy.ndarray`).
- `n` - the number that will be added to the sum of `x`, which is assumed to be 0 if not given.
Return value: the value obtained by summing `x` and adding it to `n`.
For example:
```python
import numpy as np
import mindspore as ms
@ms.jit
def func():
a = sum([0, 1, 2])
b = sum((0, 1, 2), 10)
c = sum(np.array([1, 2, 3]))
d = sum(ms.Tensor([1, 2, 3]), 10)
e = sum(ms.Tensor([[1, 2], [3, 4]]))
f = sum([1, ms.Tensor([[1, 2], [3, 4]]), ms.Tensor([[1, 2], [3, 4]])], ms.Tensor([[1, 1], [1, 1]]))
return a, b, ms.Tensor(c), d, e, f
a, b, c, d, e, f = func()
print("a: ", a)
print("b: ", b)
print("c: ", c)
print("d: ", d)
print("e: ", e)
print("f: ", f)
```
The result is as follows:
```text
a: 3
b: 13
c: 6
d: 16
e: [4 6]
f: [[ 4 6]
[ 8 10]]
```
## abs
Function: Return the absolute value of the input.
Calling: `abs(x)`.
Input parameter: - `x` - The valid types of `x` include `int`, `float`, `bool`, `complex`, `Tensor` and third-party object (such as `numpy.ndarray`).
Return value: the absolute value of the input.
For example:
```python
import mindspore as ms
from mindspore import Tensor
@ms.jit
def func():
a = abs(-45)
b = abs(100.12)
c = abs(Tensor([-1, 2]).asnumpy())
return a, b, c
a, b, c = func()
print("a: ", a)
print("b: {:.2f}".format(b))
print("c: ", c)
```
The result is as follows:
```text
a: 45
b: 100.12
c: [1 2]
```
'abs(Tensor([-1, 2]).asnumpy())' is a high-level usage, and more introduction can be found in the [AST Extended Syntaxes (LAX level)](https://www.mindspore.cn/tutorials/en/master/compile/static_graph.html#ast-extended-syntaxes-lax-level) chapter.
## map
Function: Maps one or more sequences based on the provided functions and generates a new sequence based on the mapping result. The current requirement is that the number of elements in multiple sequences be the same.
Calling: `map(func, sequence, ...)`.
Input parameters:
- `func` - Function.
- `sequence` - One or more sequences (`Tuple` or `List`).
Return value: Return a new sequence.
For example:
```python
import mindspore as ms
def add(x, y):
return x + y
@ms.jit()
def test():
elements_a = (1, 2, 3)
elements_b = (4, 5, 6)
ret1 = map(add, elements_a, elements_b)
elements_c = [0, 1, 2]
elements_d = [6, 7, 8]
ret2 = map(add, elements_c, elements_d)
return ret1, ret2
ret1, ret2 = test()
print('ret1:{}'.format(ret1))
print('ret2:{}'.format(ret2))
```
The result is as follows:
```text
ret1: (5, 7, 9)
ret2: [6, 8, 10]
```
## zip
Function: Packs elements in the corresponding positions in multiple sequences into tuples, and then uses these tuples to form a new sequence. If the number of elements in each sequence is inconsistent, the length of the new sequence is the same as that of the shortest sequence.
Calling: `zip(sequence, ...)`.
Input parameter: `sequence` - One or more sequences (`Tuple` or `List`)`.
Return value: Return a new sequence.
For example:
```python
import mindspore as ms
@ms.jit()
def test():
elements_a = (1, 2, 3)
elements_b = (4, 5, 6)
ret = zip(elements_a, elements_b)
return ret
ret = test()
print('ret:{}'.format(ret))
```
The result is as follows:
```text
ret:((1, 4), (2, 5), (3, 6))
```
## range
Function: Creates a `Tuple` based on the start value, end value, and step.
Calling:
- `range(start, stop, step)`
- `range(start, stop)`
- `range(stop)`
Input parameters:
- `start` - start value of the count. The type is `int`. The default value is 0.
- `stop` - end value of the count (exclusive). The type is `int`.
- `step` - Step. The type is `int`. The default value is 1.
Return value: Return a `Tuple`.
For example:
```python
import mindspore as ms
@ms.jit()
def test():
x = range(0, 6, 2)
y = range(0, 5)
z = range(3)
return x, y, z
x, y, z = test()
print('x:{}'.format(x))
print('y:{}'.format(y))
print('z:{}'.format(z))
```
The result is as follows:
```text
x:(0, 2, 4)
y:(0, 1, 2, 3, 4)
z:(0, 1, 2)
```
## enumerate
Function: Generates an index sequence of a sequence. The index sequence contains data and the corresponding subscript.
Calling:
- `enumerate(sequence, start)`
- `enumerate(sequence)`
Input parameters:
- `sequence` - A sequence (`Tuple`, `List`, or `Tensor`).
- `start` - Start position of the subscript. The type is `int`. The default value is 0.
Return value: A `Tuple`.
For example:
```python
import mindspore as ms
import numpy as np
y = ms.Tensor(np.array([[1, 2], [3, 4], [5, 6]]))
@ms.jit()
def test():
x = (100, 200, 300, 400)
m = enumerate(x, 3)
n = enumerate(y)
return m, n
m, n = test()
print('m:{}'.format(m))
print('n:{}'.format(n))
```
The result is as follows:
```text
m:((3, 100), (4, 200), (5, 300), (6, 400))
n:((0, Tensor(shape=[2], dtype=Int64, value= [1, 2])), (1, Tensor(shape=[2], dtype=Int64, value= [3, 4])), (2, Tensor(shape=[2], dtype=Int64, value= [5, 6])))
```
## super
Function: Calls a method of the parent class (super class). Generally, the method of the parent class is called after `super`.
Calling:
- `super().xxx()`
- `super(type, self).xxx()`
Input parameters:
- `type` - Class.
- `self` - Object.
Return value: method of the parent class.
For example:
```python
import mindspore as ms
from mindspore import nn, set_context
set_context(mode=ms.GRAPH_MODE)
class FatherNet(nn.Cell):
def __init__(self, x):
super(FatherNet, self).__init__(x)
self.x = x
def construct(self, x, y):
return self.x * x
def test_father(self, x):
return self.x + x
class SingleSubNet(FatherNet):
def __init__(self, x, z):
super(SingleSubNet, self).__init__(x)
self.z = z
def construct(self, x, y):
ret_father_construct = super().construct(x, y)
ret_father_test = super(SingleSubNet, self).test_father(x)
return ret_father_construct, ret_father_test
x = 3
y = 6
z = 9
f_net = FatherNet(x)
net = SingleSubNet(x, z)
out = net(x, y)
print("out:", out)
```
The result is as follows:
```text
out: (9, 6)
```
## pow
Function: Return the power.
Calling: `pow(x, y)`
Input parameters:
- `x` - Base number, `Number`, or `Tensor`.
- `y` - Power exponent, `Number`, or `Tensor`.
Return value: `y` power of `x`, `Number`, or `Tensor`
For example:
```python
import mindspore as ms
import numpy as np
x = ms.Tensor(np.array([1, 2, 3]))
y = ms.Tensor(np.array([1, 2, 3]))
@ms.jit()
def test(x, y):
return pow(x, y)
ret = test(x, y)
print('ret:{}'.format(ret))
```
The result is as follows:
```text
ret:[ 1 4 27]
```
## print
Function: Prints logs.
Calling: `print(arg, ...)`.
Input parameter: `arg` - Information to be printed (`int`, `float`, `bool`, `String` or `Tensor`, or third-party library data types).
Return value: none
For example:
```python
import mindspore as ms
import numpy as np
x = ms.Tensor(np.array([1, 2, 3]), ms.int32)
y = ms.Tensor(3, ms.int32)
@ms.jit()
def test(x, y):
print(x)
print(y)
return x, y
ret = test(x, y)
```
The result is as follows:
```text
Tensor(shape=[3], dtype=Int32, value= [1 2 3])
Tensor(shape=[], dtype=Int32, value=3)
```
## filter
Function: According to the provided function to judge the elements of a sequence. Each element is passed into the function as a parameter in turn, and the elements whose return result is not 0 or False form a new sequence.
Calling: `filter(func, sequence)`
Input parameters:
- `func` - Function.
- `sequence` - A sequence (`Tuple` or `List`).
Return value: Return a new sequence.
For example:
```python
import mindspore as ms
def is_odd(x):
if x % 2:
return True
return False
@ms.jit()
def test():
elements1 = (1, 2, 3, 4, 5)
ret1 = filter(is_odd, elements1)
elements2 = [6, 7, 8, 9, 10]
ret2 = filter(is_odd, elements2)
return ret1, ret2
ret1, ret2 = test()
print('ret1:{}'.format(ret1))
print('ret2:{}'.format(ret2))
```
The result is as follows:
```text
ret1:[1, 3, 5]
ret2:[7, 9]
```
## type
Function: Output the type of the input parameter.
Valid inputs: number, list, tuples, dict, numpy.ndarray, constant Tensor.
Examples of code usage are as follows:
```python
import numpy as np
import mindspore as ms
@ms.jit
def func():
a = type(1)
b = type(1.0)
c = type([1, 2, 3])
d = type((1, 2, 3))
e = type({'a': 1, 'b': 2})
f = type(np.array([1, 2, 3]))
g = type(ms.Tensor([1, 2, 3]))
return a, b, c, d, e, f, g
a, b, c, d, e, f, g = func()
print("a: ", a)
print("b: ", b)
print("c: ", c)
print("d: ", d)
print("e: ", e)
print("f: ", f)
print("g: ", g)
```
```text
a: <class 'int'>
b: <class 'float'>
c: <class 'list'>
d: <class 'tuple'>
e: <class 'dict'>
f: <class 'numpy.ndarray'>
g: <class 'mindspore.common.tensor.Tensor'>
```
> There is another way to use type as a native Python function, i.e. type(name, bases, dict) returns a class object of type name, which is not supported currently because of the low usage scenario.