Prompt:
Problem:
I'm using tensorflow 2.10.0.
In the tensorflow Dataset pipeline I'd like to define a custom map function which takes a single input element (data sample) and returns multiple elements (data samples).
The code below is my attempt, along with the desired results.
I could not follow the documentation on tf.data.Dataset().flat_map() well enough to understand if it was applicable here or not.
import tensorflow as tf

tf.compat.v1.disable_eager_execution()
input = [10, 20, 30]
def my_map_func(i):
  return [[i, i+1, i+2]]       # Fyi [[i], [i+1], [i+2]] throws an exception
ds = tf.data.Dataset.from_tensor_slices(input)
ds = ds.map(map_func=lambda input: tf.compat.v1.py_func(
  func=my_map_func, inp=[input], Tout=[tf.int64]
))
element = tf.compat.v1.data.make_one_shot_iterator(ds).get_next()
result = []
with tf.compat.v1.Session() as sess:
  for _ in range(9):
    result.append(sess.run(element))
print(result)

Results:
[array([10, 11, 12]),
array([20, 21, 22]),
array([30, 31, 32])]

Desired results:
[10, 11, 12, 20, 21, 22, 30, 31, 32]

A:
<code>
import tensorflow as tf
tf.compat.v1.disable_eager_execution()

example_input = [10, 20, 30]
def f(input=example_input):
    # return the solution in this function
    # result = f(input)
    ### BEGIN SOLUTION

    ds = tf.data.Dataset.from_tensor_slices(input)
    ds = ds.flat_map(lambda x: tf.data.Dataset.from_tensor_slices([x, x + 1, x + 2]))
    element = tf.compat.v1.data.make_one_shot_iterator(ds).get_next()

    result = []
    with tf.compat.v1.Session() as sess:
        for _ in range(9):
            result.append(sess.run(element))

    return result

import tensorflow as tf
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        input = data
        tf.compat.v1.disable_eager_execution()
        ds = tf.data.Dataset.from_tensor_slices(input)
        ds = ds.flat_map(
            lambda x: tf.data.Dataset.from_tensor_slices([x, x + 1, x + 2])
        )
        element = tf.compat.v1.data.make_one_shot_iterator(ds).get_next()
        result = []
        with tf.compat.v1.Session() as sess:
            for _ in range(9):
                result.append(sess.run(element))
        return result

    def define_test_input(test_case_id):
        if test_case_id == 1:
            tf.compat.v1.disable_eager_execution()
            input = [10, 20, 30]
        elif test_case_id == 2:
            tf.compat.v1.disable_eager_execution()
            input = [20, 40, 60]
        return input

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        assert result == ans
        return 1
    except:
        return 0

exec_context = r"""
import tensorflow as tf
input = test_input
tf.compat.v1.disable_eager_execution()
def f(input):
[insert]
result = f(input)
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
I'm using tensorflow 2.10.0.
I have a tensor of lengths in tensorflow, let's say it looks like this:
[4, 3, 5, 2]

I wish to create a mask of 1s and 0s whose number of 0s correspond to the entries to this tensor, padded in front by 1s to a total length of 8. I.e. I want to create this tensor:
[[1,1,1,1,0,0,0,0],
 [1,1,1,0,0,0,0,0],
 [1,1,1,1,1,0,0,0],
 [1,1,0,0,0,0,0,0]
]

How might I do this?

A:
<code>
import tensorflow as tf

lengths = [4, 3, 5, 2]
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(lengths):
    lengths_transposed = tf.expand_dims(lengths, 1)
    range = tf.range(0, 8, 1)
    range_row = tf.expand_dims(range, 0)
    mask = tf.less(range_row, lengths_transposed)
    result = tf.where(mask, tf.ones([4, 8]), tf.zeros([4, 8]))
    return result

result = g(lengths.copy())

import tensorflow as tf
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        lengths = data
        lengths_transposed = tf.expand_dims(lengths, 1)
        range = tf.range(0, 8, 1)
        range_row = tf.expand_dims(range, 0)
        mask = tf.less(range_row, lengths_transposed)
        result = tf.where(mask, tf.ones([4, 8]), tf.zeros([4, 8]))
        return result

    def define_test_input(test_case_id):
        if test_case_id == 1:
            lengths = [4, 3, 5, 2]
        if test_case_id == 2:
            lengths = [2, 3, 4, 5]
        return lengths

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    def tensor_equal(a, b):
        if type(a) != type(b):
            return False
        if isinstance(a, type(tf.constant([]))) is not True:
            if isinstance(a, type(tf.Variable([]))) is not True:
                return False
        if a.shape != b.shape:
            return False
        if a.dtype != tf.float32:
            a = tf.cast(a, tf.float32)
        if b.dtype != tf.float32:
            b = tf.cast(b, tf.float32)
        if not tf.reduce_min(tf.cast(a == b, dtype=tf.int32)):
            return False
        return True

    try:
        assert tensor_equal(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import tensorflow as tf
lengths = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
I'm using tensorflow 2.10.0.
I have a tensor of lengths in tensorflow, let's say it looks like this:
[4, 3, 5, 2]

I wish to create a mask of 1s and 0s whose number of 0s correspond to the entries to this tensor, padded by 1s to a total length of 8. I.e. I want to create this tensor:
[[0,0,0,0,1,1,1,1],
 [0,0,0,1,1,1,1,1],
 [0,0,0,0,0,1,1,1],
 [0,0,1,1,1,1,1,1]
]

How might I do this?

A:
<code>
import tensorflow as tf

lengths = [4, 3, 5, 2]
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(lengths):
    lengths_transposed = tf.expand_dims(lengths, 1)
    range = tf.range(0, 8, 1)
    range_row = tf.expand_dims(range, 0)
    mask = tf.less(range_row, lengths_transposed)
    result = tf.where(~mask, tf.ones([4, 8]), tf.zeros([4, 8]))
    return result

result = g(lengths.copy())

import tensorflow as tf
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        lengths = data
        lengths_transposed = tf.expand_dims(lengths, 1)
        range = tf.range(0, 8, 1)
        range_row = tf.expand_dims(range, 0)
        mask = tf.less(range_row, lengths_transposed)
        result = tf.where(~mask, tf.ones([4, 8]), tf.zeros([4, 8]))
        return result

    def define_test_input(test_case_id):
        if test_case_id == 1:
            lengths = [4, 3, 5, 2]
        if test_case_id == 2:
            lengths = [2, 3, 4, 5]
        return lengths

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    def tensor_equal(a, b):
        if type(a) != type(b):
            return False
        if isinstance(a, type(tf.constant([]))) is not True:
            if isinstance(a, type(tf.Variable([]))) is not True:
                return False
        if a.shape != b.shape:
            return False
        if a.dtype != tf.float32:
            a = tf.cast(a, tf.float32)
        if b.dtype != tf.float32:
            b = tf.cast(b, tf.float32)
        if not tf.reduce_min(tf.cast(a == b, dtype=tf.int32)):
            return False
        return True

    try:
        assert tensor_equal(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import tensorflow as tf
lengths = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
I'm using tensorflow 2.10.0.
I have a tensor of lengths in tensorflow, let's say it looks like this:
[4, 3, 5, 2]

I wish to create a mask of 1s and 0s whose number of 1s correspond to the entries to this tensor, padded in front by 0s to a total length of 8. I.e. I want to create this tensor:
[[0. 0. 0. 0. 1. 1. 1. 1.]
 [0. 0. 0. 0. 0. 1. 1. 1.]
 [0. 0. 0. 1. 1. 1. 1. 1.]
 [0. 0. 0. 0. 0. 0. 1. 1.]]

How might I do this?

A:
<code>
import tensorflow as tf

lengths = [4, 3, 5, 2]
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(lengths):
    lengths = [8-x for x in lengths]
    lengths_transposed = tf.expand_dims(lengths, 1)
    range = tf.range(0, 8, 1)
    range_row = tf.expand_dims(range, 0)
    mask = tf.less(range_row, lengths_transposed)
    result = tf.where(~mask, tf.ones([4, 8]), tf.zeros([4, 8]))
    return result

result = g(lengths.copy())

import tensorflow as tf
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        lengths = data
        lengths = [8 - x for x in lengths]
        lengths_transposed = tf.expand_dims(lengths, 1)
        range = tf.range(0, 8, 1)
        range_row = tf.expand_dims(range, 0)
        mask = tf.less(range_row, lengths_transposed)
        result = tf.where(~mask, tf.ones([4, 8]), tf.zeros([4, 8]))
        return result

    def define_test_input(test_case_id):
        if test_case_id == 1:
            lengths = [4, 3, 5, 2]
        if test_case_id == 2:
            lengths = [2, 3, 4, 5]
        return lengths

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    def tensor_equal(a, b):
        if type(a) != type(b):
            return False
        if isinstance(a, type(tf.constant([]))) is not True:
            if isinstance(a, type(tf.Variable([]))) is not True:
                return False
        if a.shape != b.shape:
            return False
        if a.dtype != tf.float32:
            a = tf.cast(a, tf.float32)
        if b.dtype != tf.float32:
            b = tf.cast(b, tf.float32)
        if not tf.reduce_min(tf.cast(a == b, dtype=tf.int32)):
            return False
        return True

    try:
        assert tensor_equal(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import tensorflow as tf
lengths = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
I'm using tensorflow 2.10.0.
I have a tensor of lengths in tensorflow, let's say it looks like this:
[4, 3, 5, 2]

I wish to create a mask of 1s and 0s whose number of 1s correspond to the entries to this tensor, padded by 0s to a total length of 8. I.e. I want to create this tensor:
[[1,1,1,1,0,0,0,0],
 [1,1,1,0,0,0,0,0],
 [1,1,1,1,1,0,0,0],
 [1,1,0,0,0,0,0,0]
]

How might I do this?

A:
<code>
import tensorflow as tf

example_lengths = [4, 3, 5, 2]
def f(lengths=example_lengths):
    # return the solution in this function
    # result = f(lengths)
    ### BEGIN SOLUTION

import tensorflow as tf
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        lengths = data
        lengths_transposed = tf.expand_dims(lengths, 1)
        range = tf.range(0, 8, 1)
        range_row = tf.expand_dims(range, 0)
        mask = tf.less(range_row, lengths_transposed)
        result = tf.where(mask, tf.ones([4, 8]), tf.zeros([4, 8]))
        return result

    def define_test_input(test_case_id):
        if test_case_id == 1:
            lengths = [4, 3, 5, 2]
        if test_case_id == 2:
            lengths = [2, 3, 4, 5]
        return lengths

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    def tensor_equal(a, b):
        if type(a) != type(b):
            return False
        if isinstance(a, type(tf.constant([]))) is not True:
            if isinstance(a, type(tf.Variable([]))) is not True:
                return False
        if a.shape != b.shape:
            return False
        if a.dtype != tf.float32:
            a = tf.cast(a, tf.float32)
        if b.dtype != tf.float32:
            b = tf.cast(b, tf.float32)
        if not tf.reduce_min(tf.cast(a == b, dtype=tf.int32)):
            return False
        return True

    try:
        assert tensor_equal(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import tensorflow as tf
lengths = test_input
def f(lengths):
[insert]
result = f(lengths)
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
I'm using tensorflow 2.10.0.
I have a tensor of lengths in tensorflow, let's say it looks like this:
[4, 3, 5, 2]

I wish to create a mask of 1s and 0s whose number of 0s correspond to the entries to this tensor, padded in front by 1s to a total length of 8. I.e. I want to create this tensor:
[[1. 1. 1. 1. 0. 0. 0. 0.]
 [1. 1. 1. 1. 1. 0. 0. 0.]
 [1. 1. 1. 0. 0. 0. 0. 0.]
 [1. 1. 1. 1. 1. 1. 0. 0.]]

How might I do this?

A:
<code>
import tensorflow as tf

lengths = [4, 3, 5, 2]
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(lengths):
    lengths = [8-x for x in lengths]
    lengths_transposed = tf.expand_dims(lengths, 1)
    range = tf.range(0, 8, 1)
    range_row = tf.expand_dims(range, 0)
    mask = tf.less(range_row, lengths_transposed)
    result = tf.where(mask, tf.ones([4, 8]), tf.zeros([4, 8]))
    return result

result = g(lengths.copy())

import tensorflow as tf
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        lengths = data
        lengths = [8 - x for x in lengths]
        lengths_transposed = tf.expand_dims(lengths, 1)
        range = tf.range(0, 8, 1)
        range_row = tf.expand_dims(range, 0)
        mask = tf.less(range_row, lengths_transposed)
        result = tf.where(mask, tf.ones([4, 8]), tf.zeros([4, 8]))
        return result

    def define_test_input(test_case_id):
        if test_case_id == 1:
            lengths = [4, 3, 5, 2]
        if test_case_id == 2:
            lengths = [2, 3, 4, 5]
        return lengths

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    def tensor_equal(a, b):
        if type(a) != type(b):
            return False
        if isinstance(a, type(tf.constant([]))) is not True:
            if isinstance(a, type(tf.Variable([]))) is not True:
                return False
        if a.shape != b.shape:
            return False
        if a.dtype != tf.float32:
            a = tf.cast(a, tf.float32)
        if b.dtype != tf.float32:
            b = tf.cast(b, tf.float32)
        if not tf.reduce_min(tf.cast(a == b, dtype=tf.int32)):
            return False
        return True

    try:
        assert tensor_equal(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import tensorflow as tf
lengths = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
I'm using tensorflow 2.10.0.
Is there any easy way to do cartesian product in Tensorflow like itertools.product? I want to get combination of elements of two tensors (a and b), in Python it is possible via itertools as list(product(a, b)). I am looking for an alternative in Tensorflow.

A:
<code>
import tensorflow as tf

a = tf.constant([1,2,3])
b = tf.constant([4,5,6,7])
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(a,b):
    tile_a = tf.tile(tf.expand_dims(a, 1), [1, tf.shape(b)[0]])
    tile_a = tf.expand_dims(tile_a, 2)
    tile_b = tf.tile(tf.expand_dims(b, 0), [tf.shape(a)[0], 1])
    tile_b = tf.expand_dims(tile_b, 2)
    cart = tf.concat([tile_a, tile_b], axis=2)
    return cart

result = g(a.__copy__(),b.__copy__())

import tensorflow as tf
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        data = data
        a, b = data
        tile_a = tf.tile(tf.expand_dims(a, 1), [1, tf.shape(b)[0]])
        tile_a = tf.expand_dims(tile_a, 2)
        tile_b = tf.tile(tf.expand_dims(b, 0), [tf.shape(a)[0], 1])
        tile_b = tf.expand_dims(tile_b, 2)
        cart = tf.concat([tile_a, tile_b], axis=2)
        return cart

    def define_test_input(test_case_id):
        if test_case_id == 1:
            a = tf.constant([1, 2, 3])
            b = tf.constant([4, 5, 6, 7])
        return a, b

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    def tensor_equal(a, b):
        if type(a) != type(b):
            return False
        if isinstance(a, type(tf.constant([]))) is not True:
            if isinstance(a, type(tf.Variable([]))) is not True:
                return False
        if a.shape != b.shape:
            return False
        if a.dtype != tf.float32:
            a = tf.cast(a, tf.float32)
        if b.dtype != tf.float32:
            b = tf.cast(b, tf.float32)
        if not tf.reduce_min(tf.cast(a == b, dtype=tf.int32)):
            return False
        return True

    try:
        assert tensor_equal(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import tensorflow as tf
a,b = test_input
[insert]
if result.shape == [12,2]:
    result = tf.reshape(result, [3,4,2])
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
I'm using tensorflow 2.10.0.
Is there any easy way to do cartesian product in Tensorflow like itertools.product? I want to get combination of elements of two tensors (a and b), in Python it is possible via itertools as list(product(a, b)). I am looking for an alternative in Tensorflow.

A:
<code>
import tensorflow as tf

example_a = tf.constant([1,2,3])
example_b = tf.constant([4,5,6,7])
def f(a=example_a,b=example_b):
    # return the solution in this function
    # result = f(a,b)
    ### BEGIN SOLUTION

    tile_a = tf.tile(tf.expand_dims(a, 1), [1, tf.shape(b)[0]])
    tile_a = tf.expand_dims(tile_a, 2)
    tile_b = tf.tile(tf.expand_dims(b, 0), [tf.shape(a)[0], 1])
    tile_b = tf.expand_dims(tile_b, 2)
    cart = tf.concat([tile_a, tile_b], axis=2)
    result = cart

    return result

import tensorflow as tf
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        data = data
        a, b = data
        tile_a = tf.tile(tf.expand_dims(a, 1), [1, tf.shape(b)[0]])
        tile_a = tf.expand_dims(tile_a, 2)
        tile_b = tf.tile(tf.expand_dims(b, 0), [tf.shape(a)[0], 1])
        tile_b = tf.expand_dims(tile_b, 2)
        cart = tf.concat([tile_a, tile_b], axis=2)
        return cart

    def define_test_input(test_case_id):
        if test_case_id == 1:
            a = tf.constant([1, 2, 3])
            b = tf.constant([4, 5, 6, 7])
        return a, b

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    def tensor_equal(a, b):
        if type(a) != type(b):
            return False
        if isinstance(a, type(tf.constant([]))) is not True:
            if isinstance(a, type(tf.Variable([]))) is not True:
                return False
        if a.shape != b.shape:
            return False
        if a.dtype != tf.float32:
            a = tf.cast(a, tf.float32)
        if b.dtype != tf.float32:
            b = tf.cast(b, tf.float32)
        if not tf.reduce_min(tf.cast(a == b, dtype=tf.int32)):
            return False
        return True

    try:
        assert tensor_equal(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import tensorflow as tf
a,b = test_input
def f(a,b):
[insert]
result = f(a,b)
if result.shape == [12,2]:
    result = tf.reshape(result, [3,4,2])
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
I'm using tensorflow 2.10.0.
I have a tensor that have shape (50, 100, 1, 512) and i want to reshape it or drop the third dimension so that the new tensor have shape (50, 100, 512).
a = tf.constant(np.random.rand(50, 100, 1, 512))

How can i solve it. Thanks

A:
<code>
import tensorflow as tf
import numpy as np

np.random.seed(10)
a = tf.constant(np.random.rand(50, 100, 1, 512))
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(a):
    return tf.squeeze(a)

result = g(a.__copy__())

import tensorflow as tf
import numpy as np
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        a = data
        return tf.squeeze(a)

    def define_test_input(test_case_id):
        if test_case_id == 1:
            np.random.seed(10)
            a = tf.constant(np.random.rand(5, 10, 1, 51))
        if test_case_id == 2:
            np.random.seed(10)
            a = tf.constant(np.random.rand(5, 2, 1, 5))
        return a

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    def tensor_equal(a, b):
        if type(a) != type(b):
            return False
        if isinstance(a, type(tf.constant([]))) is not True:
            if isinstance(a, type(tf.Variable([]))) is not True:
                return False
        if a.shape != b.shape:
            return False
        if a.dtype != tf.float32:
            a = tf.cast(a, tf.float32)
        if b.dtype != tf.float32:
            b = tf.cast(b, tf.float32)
        if not tf.reduce_min(tf.cast(a == b, dtype=tf.int32)):
            return False
        return True

    try:
        assert tensor_equal(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import tensorflow as tf
import numpy as np
a = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
I'm using tensorflow 2.10.0.
I have a tensor that have shape (50, 100, 512) and i want to reshape it or add a new dimension so that the new tensor have shape (50, 100, 1, 512).
a = tf.constant(np.random.rand(50, 100, 512))

How can I solve it. Thanks

A:
<code>
import tensorflow as tf
import numpy as np

np.random.seed(10)
a = tf.constant(np.random.rand(50, 100, 512))
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(a):
    return tf.expand_dims(a, 2)

result = g(a.__copy__())

import tensorflow as tf
import numpy as np
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        a = data
        return tf.expand_dims(a, 2)

    def define_test_input(test_case_id):
        if test_case_id == 1:
            np.random.seed(10)
            a = tf.constant(np.random.rand(5, 10, 52))
        if test_case_id == 2:
            np.random.seed(10)
            a = tf.constant(np.random.rand(5, 10, 5))
        return a

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    def tensor_equal(a, b):
        if type(a) != type(b):
            return False
        if isinstance(a, type(tf.constant([]))) is not True:
            if isinstance(a, type(tf.Variable([]))) is not True:
                return False
        if a.shape != b.shape:
            return False
        if a.dtype != tf.float32:
            a = tf.cast(a, tf.float32)
        if b.dtype != tf.float32:
            b = tf.cast(b, tf.float32)
        if not tf.reduce_min(tf.cast(a == b, dtype=tf.int32)):
            return False
        return True

    try:
        assert tensor_equal(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import tensorflow as tf
import numpy as np
a = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
I'm using tensorflow 2.10.0.
I have a tensor that have shape (50, 100, 512) and i want to reshape it or add two new dimensions so that the new tensor have shape (1, 50, 100, 1, 512).
a = tf.constant(np.random.rand(50, 100, 512))

How can I solve it. Thanks

A:
<code>
import tensorflow as tf
import numpy as np

np.random.seed(10)
a = tf.constant(np.random.rand(50, 100, 512))
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(a):
    return tf.expand_dims(tf.expand_dims(a, 2), 0)

result = g(a.__copy__())

import tensorflow as tf
import numpy as np
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        a = data
        return tf.expand_dims(tf.expand_dims(a, 2), 0)

    def define_test_input(test_case_id):
        if test_case_id == 1:
            np.random.seed(10)
            a = tf.constant(np.random.rand(5, 10, 52))
        if test_case_id == 2:
            np.random.seed(10)
            a = tf.constant(np.random.rand(5, 10, 5))
        return a

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    def tensor_equal(a, b):
        if type(a) != type(b):
            return False
        if isinstance(a, type(tf.constant([]))) is not True:
            if isinstance(a, type(tf.Variable([]))) is not True:
                return False
        if a.shape != b.shape:
            return False
        if a.dtype != tf.float32:
            a = tf.cast(a, tf.float32)
        if b.dtype != tf.float32:
            b = tf.cast(b, tf.float32)
        if not tf.reduce_min(tf.cast(a == b, dtype=tf.int32)):
            return False
        return True

    try:
        assert tensor_equal(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import tensorflow as tf
import numpy as np
a = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
I'm using tensorflow 2.10.0.
What is the equivalent of the following in Tensorflow?
np.sum(A, axis=1)
I want to get a tensor.

A:
<code>
import tensorflow as tf
import numpy as np

np.random.seed(10)
A = tf.constant(np.random.randint(100,size=(5, 3)))
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(A):
    return tf.reduce_sum(A, 1)

result = g(A.__copy__())

import tensorflow as tf
import numpy as np
import copy
import tokenize, io

def generate_test_case(test_case_id):
    def generate_ans(data):
        A = data
        return tf.reduce_sum(A, 1)

    def define_test_input(test_case_id):
        if test_case_id == 1:
            np.random.seed(10)
            A = tf.constant(np.random.randint(100, size=(5, 3)))
        return A

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    def tensor_equal(a, b):
        if type(a) != type(b):
            return False
        if isinstance(a, type(tf.constant([]))) is not True:
            if isinstance(a, type(tf.Variable([]))) is not True:
                return False
        if a.shape != b.shape:
            return False
        if a.dtype != tf.float32:
            a = tf.cast(a, tf.float32)
        if b.dtype != tf.float32:
            b = tf.cast(b, tf.float32)
        if not tf.reduce_min(tf.cast(a == b, dtype=tf.int32)):
            return False
        return True

    try:
        assert tensor_equal(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import tensorflow as tf
import numpy as np
A = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "tf" in tokens and "reduce_sum" in tokens


Prompt:
Problem:
I'm using tensorflow 2.10.0.
What is the equivalent of the following in Tensorflow?
np.prod(A, axis=1)
I want to get a tensor.

A:
<code>
import tensorflow as tf
import numpy as np

np.random.seed(10)
A = tf.constant(np.random.randint(100,size=(5, 3)))
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(A):
    return tf.reduce_prod(A, 1)

result = g(A.__copy__())

import tensorflow as tf
import numpy as np
import copy
import tokenize, io

def generate_test_case(test_case_id):
    def generate_ans(data):
        A = data
        return tf.reduce_prod(A, 1)

    def define_test_input(test_case_id):
        if test_case_id == 1:
            np.random.seed(10)
            A = tf.constant(np.random.randint(100, size=(5, 3)))
        return A

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    def tensor_equal(a, b):
        if type(a) != type(b):
            return False
        if isinstance(a, type(tf.constant([]))) is not True:
            if isinstance(a, type(tf.Variable([]))) is not True:
                return False
        if a.shape != b.shape:
            return False
        if a.dtype != tf.float32:
            a = tf.cast(a, tf.float32)
        if b.dtype != tf.float32:
            b = tf.cast(b, tf.float32)
        if not tf.reduce_min(tf.cast(a == b, dtype=tf.int32)):
            return False
        return True

    try:
        assert tensor_equal(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import tensorflow as tf
import numpy as np
A = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "tf" in tokens and "reduce_prod" in tokens


Prompt:
Problem:
I'm using tensorflow 2.10.0.
What is the equivalent of the following in Tensorflow?
np.reciprocal(A)
I want to get a tensor.

A:
<code>
import tensorflow as tf

A = tf.constant([-0.5, -0.1, 0, 0.1, 0.5, 2], dtype=tf.float32)
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(A):
    return tf.math.reciprocal(A)

result = g(A.__copy__())

import tensorflow as tf
import numpy as np
import copy
import tokenize, io

def generate_test_case(test_case_id):
    def generate_ans(data):
        A = data
        return tf.math.reciprocal(A)

    def define_test_input(test_case_id):
        if test_case_id == 1:
            np.random.seed(10)
            A = tf.constant([-0.5, -0.1, 0, 0.1, 0.5, 2], dtype=tf.float32)
        return A

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    def tensor_equal(a, b):
        if type(a) != type(b):
            return False
        if isinstance(a, type(tf.constant([]))) is not True:
            if isinstance(a, type(tf.Variable([]))) is not True:
                return False
        if a.shape != b.shape:
            return False
        if a.dtype != tf.float32:
            a = tf.cast(a, tf.float32)
        if b.dtype != tf.float32:
            b = tf.cast(b, tf.float32)
        if not tf.reduce_min(tf.cast(a == b, dtype=tf.int32)):
            return False
        return True

    try:
        assert tensor_equal(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import numpy as np
import tensorflow as tf
A = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "tf" in tokens and "math" in tokens and "reciprocal" in tokens


Prompt:
Problem:
I'm using tensorflow 2.10.0.
I have two embeddings tensor A and B, which looks like
[
  [1,1,1],
  [1,1,1]
]

and
[
  [0,0,0],
  [1,1,1]
]

what I want to do is calculate the L2 distance d(A,B) element-wise.
First I did a tf.square(tf.sub(lhs, rhs)) to get
[
  [1,1,1],
  [0,0,0]
]

and then I want to do an element-wise reduce which returns
[
  3,
  0
]

but tf.reduce_sum does not allow my to reduce by row. Any inputs would be appreciated. Thanks.

A:
<code>
import tensorflow as tf

a = tf.constant([
  [1,1,1],
  [1,1,1]
])
b = tf.constant([
  [0,0,0],
  [1,1,1]
])
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(a,b):
    return tf.reduce_sum(tf.square( tf.subtract( a, b)), 1)

result = g(a.__copy__(),b.__copy__())

import tensorflow as tf
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        data = data
        a, b = data
        return tf.reduce_sum(tf.square(tf.subtract(a, b)), 1)

    def define_test_input(test_case_id):
        if test_case_id == 1:
            a = tf.constant([[1, 1, 1], [1, 1, 1]])
            b = tf.constant([[0, 0, 0], [1, 1, 1]])
        if test_case_id == 2:
            a = tf.constant([[0, 1, 1], [1, 0, 1]])
            b = tf.constant([[0, 0, 0], [1, 1, 1]])
        return a, b

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    def tensor_equal(a, b):
        if type(a) != type(b):
            return False
        if isinstance(a, type(tf.constant([]))) is not True:
            if isinstance(a, type(tf.Variable([]))) is not True:
                return False
        if a.shape != b.shape:
            return False
        if a.dtype != tf.float32:
            a = tf.cast(a, tf.float32)
        if b.dtype != tf.float32:
            b = tf.cast(b, tf.float32)
        if not tf.reduce_min(tf.cast(a == b, dtype=tf.int32)):
            return False
        return True

    try:
        assert tensor_equal(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import tensorflow as tf
a,b = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
I'm using tensorflow 2.10.0.
I have two embeddings tensor A and B, which looks like
[
  [1,1,1],
  [1,1,1]
]

and
[
  [0,0,0],
  [1,1,1]
]

what I want to do is calculate the L2 distance d(A,B) column-wise.
First I did a tf.square(tf.sub(lhs, rhs)) to get
[
  [1,1,1],
  [0,0,0]
]

and then I want to do an column-wise reduce which returns
[
  1,1,1
]

but tf.reduce_sum does not allow my to reduce by column. Any inputs would be appreciated. Thanks.

A:
<code>
import tensorflow as tf

a = tf.constant([
  [1,1,1],
  [0,1,1]
])
b = tf.constant([
  [0,0,1],
  [1,1,1]
])
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(a,b):
    return tf.reduce_sum(tf.square( tf.subtract( a, b)), 0)

result = g(a.__copy__(),b.__copy__())

import tensorflow as tf
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        a, b = data
        return tf.reduce_sum(tf.square(tf.subtract(a, b)), 0)

    def define_test_input(test_case_id):
        if test_case_id == 1:
            a = tf.constant([[1, 1, 1], [1, 1, 1]])
            b = tf.constant([[0, 0, 0], [1, 1, 1]])
        if test_case_id == 2:
            a = tf.constant([[0, 1, 1], [1, 0, 1]])
            b = tf.constant([[0, 0, 0], [1, 1, 1]])
        return a, b

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    def tensor_equal(a, b):
        if type(a) != type(b):
            return False
        if isinstance(a, type(tf.constant([]))) is not True:
            if isinstance(a, type(tf.Variable([]))) is not True:
                return False
        if a.shape != b.shape:
            return False
        if a.dtype != tf.float32:
            a = tf.cast(a, tf.float32)
        if b.dtype != tf.float32:
            b = tf.cast(b, tf.float32)
        if not tf.reduce_min(tf.cast(a == b, dtype=tf.int32)):
            return False
        return True

    try:
        assert tensor_equal(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import tensorflow as tf
a,b = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
I'm using tensorflow 2.10.0.
I have two embeddings tensor A and B, which looks like
[
  [1,1,1],
  [1,1,1]
]

and
[
  [0,0,0],
  [1,1,1]
]

what I want to do is calculate the L2 distance d(A,B) element-wise.
First I did a tf.square(tf.sub(lhs, rhs)) to get
[
  [1,1,1],
  [0,0,0]
]

and then I want to do an element-wise reduce which returns
[
  3,
  0
]

but tf.reduce_sum does not allow my to reduce by row. Any inputs would be appreciated. Thanks.

A:
<code>
import tensorflow as tf

example_a = tf.constant([
  [1,1,1],
  [1,1,1]
])
example_b = tf.constant([
  [0,0,0],
  [1,1,1]
])
def f(A=example_a,B=example_b):
    # return the solution in this function
    # result = f(A,B)
    ### BEGIN SOLUTION

    result = tf.reduce_sum(tf.square( tf.subtract( A, B)), 1)

    return result

import tensorflow as tf
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        a, b = data
        return tf.reduce_sum(tf.square(tf.subtract(a, b)), 1)

    def define_test_input(test_case_id):
        if test_case_id == 1:
            a = tf.constant([[1, 1, 1], [1, 1, 1]])
            b = tf.constant([[0, 0, 0], [1, 1, 1]])
        if test_case_id == 2:
            a = tf.constant([[0, 1, 1], [1, 0, 1]])
            b = tf.constant([[0, 0, 0], [1, 1, 1]])
        return a, b

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    def tensor_equal(a, b):
        if type(a) != type(b):
            return False
        if isinstance(a, type(tf.constant([]))) is not True:
            if isinstance(a, type(tf.Variable([]))) is not True:
                return False
        if a.shape != b.shape:
            return False
        if a.dtype != tf.float32:
            a = tf.cast(a, tf.float32)
        if b.dtype != tf.float32:
            b = tf.cast(b, tf.float32)
        if not tf.reduce_min(tf.cast(a == b, dtype=tf.int32)):
            return False
        return True

    try:
        assert tensor_equal(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import tensorflow as tf
A,B = test_input
def f(A,B):
[insert]
result = f(A,B)
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
I'm using tensorflow 2.10.0.

import tensorflow as tf
x = [[1,2,3],[4,5,6]]
y = [0,1]
z = [1,2]
x = tf.constant(x)
y = tf.constant(y)
z = tf.constant(z)
m = x[y,z]

What I expect is m = [2,6]
I can get the result by theano or numpy. How I get the result using tensorflow?

A:
<code>
import tensorflow as tf

x = [[1,2,3],[4,5,6]]
y = [0,1]
z = [1,2]
x = tf.constant(x)
y = tf.constant(y)
z = tf.constant(z)
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(x,y,z):
    return tf.gather_nd(x, [y, z])

result = g(x.__copy__(),y.__copy__(),z.__copy__())

import tensorflow as tf
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        x, y, z = data
        return tf.gather_nd(x, [y, z])

    def define_test_input(test_case_id):
        if test_case_id == 1:
            x = [[1, 2, 3], [4, 5, 6]]
            y = [0, 1]
            z = [1, 2]
            x = tf.constant(x)
            y = tf.constant(y)
            z = tf.constant(z)
        if test_case_id == 2:
            x = [[1, 2, 3], [4, 5, 6]]
            y = [0, 1]
            z = [1, 0]
            x = tf.constant(x)
            y = tf.constant(y)
            z = tf.constant(z)
        return x, y, z

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    def tensor_equal(a, b):
        if type(a) != type(b):
            return False
        if isinstance(a, type(tf.constant([]))) is not True:
            if isinstance(a, type(tf.Variable([]))) is not True:
                return False
        if a.shape != b.shape:
            return False
        if a.dtype != tf.float32:
            a = tf.cast(a, tf.float32)
        if b.dtype != tf.float32:
            b = tf.cast(b, tf.float32)
        if not tf.reduce_min(tf.cast(a == b, dtype=tf.int32)):
            return False
        return True

    try:
        assert tensor_equal(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import tensorflow as tf
x,y,z = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
I'm using tensorflow 2.10.0.

import tensorflow as tf
x = [[1,2,3],[4,5,6]]
row = [0,1]
col = [0,2]
x = tf.constant(x)
row = tf.constant(row)
col = tf.constant(col)
m = x[[row,col]]

What I expect is m = [1,6]
I can get the result by theano or numpy. How I get the result using tensorflow?

A:
<code>
import tensorflow as tf

x = [[1,2,3],[4,5,6]]
row = [0,0]
col = [1,2]
x = tf.constant(x)
row = tf.constant(row)
col = tf.constant(col)
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(x,row,col):
    index = [[row[i],col[i]] for i in range(len(row))]
    return tf.gather_nd(x, index)

result = g(x.__copy__(),row.__copy__(),col.__copy__())

import tensorflow as tf
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        x, row, col = data
        index = [[row[i], col[i]] for i in range(len(col))]
        return tf.gather_nd(x, index)

    def define_test_input(test_case_id):
        if test_case_id == 1:
            x = [[1, 2, 3], [4, 5, 6]]
            row = [0, 0]
            col = [1, 2]
            x = tf.constant(x)
            row = tf.constant(row)
            col = tf.constant(col)
        if test_case_id == 2:
            x = [[1, 2, 3], [4, 5, 6]]
            row = [1, 0]
            col = [1, 2]
            x = tf.constant(x)
            row = tf.constant(row)
            col = tf.constant(col)
        return x, row, col

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    def tensor_equal(a, b):
        if type(a) != type(b):
            return False
        if isinstance(a, type(tf.constant([]))) is not True:
            if isinstance(a, type(tf.Variable([]))) is not True:
                return False
        if a.shape != b.shape:
            return False
        if a.dtype != tf.float32:
            a = tf.cast(a, tf.float32)
        if b.dtype != tf.float32:
            b = tf.cast(b, tf.float32)
        if not tf.reduce_min(tf.cast(a == b, dtype=tf.int32)):
            return False
        return True

    try:
        assert tensor_equal(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import tensorflow as tf
x,row,col = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
I'm using tensorflow 2.10.0.

import tensorflow as tf
x = [[1,2,3],[4,5,6]]
y = [0,1]
z = [1,2]
x = tf.constant(x)
y = tf.constant(y)
z = tf.constant(z)
m = x[y,z]

What I expect is m = [2,6]
I can get the result by theano or numpy. How I get the result using tensorflow?

A:
<code>
import tensorflow as tf

example_x = [[1,2,3],[4,5,6]]
example_y = [0,1]
example_z = [1,2]
example_x = tf.constant(example_x)
example_y = tf.constant(example_y)
example_z = tf.constant(example_z)
def f(x=example_x,y=example_y,z=example_z):
    # return the solution in this function
    # result = f(x,y,z)
    ### BEGIN SOLUTION

    result = tf.gather_nd(x, [y, z])

    return result

import tensorflow as tf
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        x, y, z = data
        return tf.gather_nd(x, [y, z])

    def define_test_input(test_case_id):
        if test_case_id == 1:
            x = [[1, 2, 3], [4, 5, 6]]
            y = [0, 1]
            z = [1, 2]
            x = tf.constant(x)
            y = tf.constant(y)
            z = tf.constant(z)
        if test_case_id == 2:
            x = [[1, 2, 3], [4, 5, 6]]
            y = [0, 1]
            z = [1, 0]
            x = tf.constant(x)
            y = tf.constant(y)
            z = tf.constant(z)
        return x, y, z

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    def tensor_equal(a, b):
        if type(a) != type(b):
            return False
        if isinstance(a, type(tf.constant([]))) is not True:
            if isinstance(a, type(tf.Variable([]))) is not True:
                return False
        if a.shape != b.shape:
            return False
        if a.dtype != tf.float32:
            a = tf.cast(a, tf.float32)
        if b.dtype != tf.float32:
            b = tf.cast(b, tf.float32)
        if not tf.reduce_min(tf.cast(a == b, dtype=tf.int32)):
            return False
        return True

    try:
        assert tensor_equal(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import tensorflow as tf
x,y,z = test_input
def f(x,y,z):
[insert]
result = f(x,y,z)
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
I'm using tensorflow 2.10.0.
I have two 3D tensors, tensor A which has shape [B,N,S] and tensor B which also has shape [B,N,S]. What I want to get is a third tensor C, which I expect to have [B,B,N] shape, where the element C[i,j,k] = np.dot(A[i,k,:], B[j,k,:]. I also want to achieve this is a vectorized way.
Some further info: The two tensors A and B have shape [Batch_size, Num_vectors, Vector_size]. The tensor C, is supposed to represent the dot product between each element in the batch from A and each element in the batch from B, between all of the different vectors.
Hope that it is clear enough and looking forward to you answers!

A:
<code>
import tensorflow as tf
import numpy as np

np.random.seed(10)
A = tf.constant(np.random.randint(low=0, high=5, size=(10, 20, 30)))
B = tf.constant(np.random.randint(low=0, high=5, size=(10, 20, 30)))
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

import numpy as np
def g(A,B):
    return tf.constant(np.einsum( 'ikm, jkm-> ijk', A, B))

result = g(A.__copy__(),B.__copy__())

import tensorflow as tf
import numpy as np
import copy
import tokenize, io

def generate_test_case(test_case_id):
    def generate_ans(data):
        A, B = data
        return tf.constant(np.einsum("ikm, jkm-> ijk", A, B))

    def define_test_input(test_case_id):
        if test_case_id == 1:
            np.random.seed(10)
            A = tf.constant(np.random.randint(low=0, high=5, size=(10, 20, 30)))
            B = tf.constant(np.random.randint(low=0, high=5, size=(10, 20, 30)))
        return A, B

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    def tensor_equal(a, b):
        if type(a) != type(b):
            return False
        if isinstance(a, type(tf.constant([]))) is not True:
            if isinstance(a, type(tf.Variable([]))) is not True:
                return False
        if a.shape != b.shape:
            return False
        if a.dtype != tf.float32:
            a = tf.cast(a, tf.float32)
        if b.dtype != tf.float32:
            b = tf.cast(b, tf.float32)
        if not tf.reduce_min(tf.cast(a == b, dtype=tf.int32)):
            return False
        return True

    try:
        assert tensor_equal(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import tensorflow as tf
import numpy as np
A,B = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "for" not in tokens and "while" not in tokens


Prompt:
Problem:
I'm using tensorflow 2.10.0.
I have two 3D tensors, tensor A which has shape [B,N,S] and tensor B which also has shape [B,N,S]. What I want to get is a third tensor C, which I expect to have [B,N,N] shape, where the element C[i,j,k] = np.dot(A[i,j,:], B[i,k,:]. I also want to achieve this is a vectorized way.
Some further info: The two tensors A and B have shape [Batch_size, Num_vectors, Vector_size]. The tensor C, is supposed to represent the dot product between each element in the batch from A and each element in the batch from B, between all of the different vectors.
Hope that it is clear enough and looking forward to you answers!

A:
<code>
import tensorflow as tf
import numpy as np

np.random.seed(10)
A = tf.constant(np.random.randint(low=0, high=5, size=(10, 20, 30)))
B = tf.constant(np.random.randint(low=0, high=5, size=(10, 20, 30)))
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

import numpy as np
def g(A,B):
    return tf.constant(np.einsum('ijm, ikm-> ijk', A, B))

result = g(A.__copy__(),B.__copy__())

import tensorflow as tf
import numpy as np
import copy
import tokenize, io

def generate_test_case(test_case_id):
    def generate_ans(data):
        A, B = data
        return tf.constant(np.einsum("ijm, ikm-> ijk", A, B))

    def define_test_input(test_case_id):
        if test_case_id == 1:
            np.random.seed(10)
            A = tf.constant(np.random.randint(low=0, high=5, size=(10, 20, 30)))
            B = tf.constant(np.random.randint(low=0, high=5, size=(10, 20, 30)))
        return A, B

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    def tensor_equal(a, b):
        if type(a) != type(b):
            return False
        if isinstance(a, type(tf.constant([]))) is not True:
            if isinstance(a, type(tf.Variable([]))) is not True:
                return False
        if a.shape != b.shape:
            return False
        if a.dtype != tf.float32:
            a = tf.cast(a, tf.float32)
        if b.dtype != tf.float32:
            b = tf.cast(b, tf.float32)
        if not tf.reduce_min(tf.cast(a == b, dtype=tf.int32)):
            return False
        return True

    try:
        assert tensor_equal(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import tensorflow as tf
import numpy as np
A,B = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "for" not in tokens and "while" not in tokens


Prompt:
Problem:
I'm using tensorflow 2.10.0.
I have a list of bytes and I want to convert it to a list of strings, in python I use this decode function:
x=[b'\xd8\xa8\xd9\x85\xd8\xb3\xd8\xa3\xd9\x84\xd8\xa9',
    b'\xd8\xa5\xd9\x86\xd8\xb4\xd8\xa7\xd8\xa1',
    b'\xd9\x82\xd8\xb6\xd8\xa7\xd8\xa1',
    b'\xd8\xac\xd9\x86\xd8\xa7\xd8\xa6\xd9\x8a',
    b'\xd8\xaf\xd9\x88\xd9\x84\xd9\x8a']

How can I get the string result list in Tensorflow?
thank you

A:
<code>
import tensorflow as tf

x=[b'\xd8\xa8\xd9\x85\xd8\xb3\xd8\xa3\xd9\x84\xd8\xa9',
    b'\xd8\xa5\xd9\x86\xd8\xb4\xd8\xa7\xd8\xa1',
    b'\xd9\x82\xd8\xb6\xd8\xa7\xd8\xa1',
    b'\xd8\xac\xd9\x86\xd8\xa7\xd8\xa6\xd9\x8a',
    b'\xd8\xaf\xd9\x88\xd9\x84\xd9\x8a']
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(x):
    return [tf.compat.as_str_any(a) for a in x]

result = g(x.copy())

import tensorflow as tf
import copy
import tokenize, io

def generate_test_case(test_case_id):
    def generate_ans(data):
        x = data
        return [tf.compat.as_str_any(a) for a in x]

    def define_test_input(test_case_id):
        if test_case_id == 1:
            x = [
                b"\xd8\xa8\xd9\x85\xd8\xb3\xd8\xa3\xd9\x84\xd8\xa9",
                b"\xd8\xa5\xd9\x86\xd8\xb4\xd8\xa7\xd8\xa1",
                b"\xd9\x82\xd8\xb6\xd8\xa7\xd8\xa1",
                b"\xd8\xac\xd9\x86\xd8\xa7\xd8\xa6\xd9\x8a",
                b"\xd8\xaf\xd9\x88\xd9\x84\xd9\x8a",
            ]
        return x

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        assert result == ans
        return 1
    except:
        return 0

exec_context = r"""
import tensorflow as tf
x = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "tf" in tokens


Prompt:
Problem:
I'm using tensorflow 2.10.0.
I have a list of bytes and I want to convert it to a list of strings, in python I use this decode function:
x=[b'\xd8\xa8\xd9\x85\xd8\xb3\xd8\xa3\xd9\x84\xd8\xa9',
    b'\xd8\xa5\xd9\x86\xd8\xb4\xd8\xa7\xd8\xa1',
    b'\xd9\x82\xd8\xb6\xd8\xa7\xd8\xa1',
    b'\xd8\xac\xd9\x86\xd8\xa7\xd8\xa6\xd9\x8a',
    b'\xd8\xaf\xd9\x88\xd9\x84\xd9\x8a']

How can I get the string result list in Tensorflow?
thank you

A:
<code>
import tensorflow as tf

example_x=[b'\xd8\xa8\xd9\x85\xd8\xb3\xd8\xa3\xd9\x84\xd8\xa9',
    b'\xd8\xa5\xd9\x86\xd8\xb4\xd8\xa7\xd8\xa1',
    b'\xd9\x82\xd8\xb6\xd8\xa7\xd8\xa1',
    b'\xd8\xac\xd9\x86\xd8\xa7\xd8\xa6\xd9\x8a',
    b'\xd8\xaf\xd9\x88\xd9\x84\xd9\x8a']
def f(x=example_x):
    # return the solution in this function
    # result = f(x)
    ### BEGIN SOLUTION

    result = [tf.compat.as_str_any(a) for a in x]

    return result

import tensorflow as tf
import copy
import tokenize, io

def generate_test_case(test_case_id):
    def generate_ans(data):
        x = data
        return [tf.compat.as_str_any(a) for a in x]

    def define_test_input(test_case_id):
        if test_case_id == 1:
            x = [
                b"\xd8\xa8\xd9\x85\xd8\xb3\xd8\xa3\xd9\x84\xd8\xa9",
                b"\xd8\xa5\xd9\x86\xd8\xb4\xd8\xa7\xd8\xa1",
                b"\xd9\x82\xd8\xb6\xd8\xa7\xd8\xa1",
                b"\xd8\xac\xd9\x86\xd8\xa7\xd8\xa6\xd9\x8a",
                b"\xd8\xaf\xd9\x88\xd9\x84\xd9\x8a",
            ]
        return x

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        assert result == ans
        return 1
    except:
        return 0

exec_context = r"""
import tensorflow as tf
x = test_input
def f(x):
[insert]
result = f(x)
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "tf" in tokens


Prompt:
Problem:
I'm using tensorflow 2.10.0.
I've come across a case in which the averaging includes padded values. Given a tensor X of some shape (batch_size, ..., features), there could be zero padded features to get the same shape.
How can I average the second to last dimension of X (the features) but only the non-zero entries? So, we divide by the sum by the number of non-zero entries.
Example input:
x = [[[[1,2,3], [2,3,4], [0,0,0]],
       [[1,2,3], [2,0,4], [3,4,5]],
       [[1,2,3], [0,0,0], [0,0,0]],
       [[1,2,3], [1,2,3], [0,0,0]]],
      [[[1,2,3], [0,1,0], [0,0,0]],
       [[1,2,3], [2,3,4], [0,0,0]],
       [[1,2,3], [0,0,0], [0,0,0]],
       [[1,2,3], [1,2,3], [1,2,3]]]]
# Desired output
y = [[[1.5 2.5 3.5]
      [2.  2.  4. ]
      [1.  2.  3. ]
      [1.  2.  3. ]]
     [[0.5 1.5 1.5]
      [1.5 2.5 3.5]
      [1.  2.  3. ]
      [1.  2.  3. ]]]

A:
<code>
import tensorflow as tf

x = [[[[1, 2, 3], [2, 3, 4], [0, 0, 0]],
      [[1, 2, 3], [2, 0, 4], [3, 4, 5]],
      [[1, 2, 3], [0, 0, 0], [0, 0, 0]],
      [[1, 2, 3], [1, 2, 3], [0, 0, 0]]],
     [[[1, 2, 3], [0, 1, 0], [0, 0, 0]],
      [[1, 2, 3], [2, 3, 4], [0, 0, 0]],
      [[1, 2, 3], [0, 0, 0], [0, 0, 0]],
      [[1, 2, 3], [1, 2, 3], [1, 2, 3]]]]
x = tf.convert_to_tensor(x, dtype=tf.float32)
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(x):
    non_zero = tf.cast(x != 0, tf.float32)
    y = tf.reduce_sum(x, axis=-2) / tf.reduce_sum(non_zero, axis=-2)
    return y

result = g(x.__copy__())


Prompt:
Problem:
I'm using tensorflow 2.10.0.
I've come across a case in which the averaging includes padded values. Given a tensor X of some shape (batch_size, ..., features), there could be zero padded features to get the same shape.
How can I variance the second to last dimension of X (the features) but only the non-zero entries? Example input:
x = [[[[1,2,3], [2,3,4], [0,0,0]],
       [[1,2,3], [2,0,4], [3,4,5]],
       [[1,2,3], [0,0,0], [0,0,0]],
       [[1,2,3], [1,2,3], [0,0,0]]],
      [[[1,2,3], [0,1,0], [0,0,0]],
       [[1,2,3], [2,3,4], [0,0,0]],
       [[1,2,3], [0,0,0], [0,0,0]],
       [[1,2,3], [1,2,3], [1,2,3]]]]
# Desired output
y = [[[0.25       0.25       0.25      ]
  [0.6666665  1.         0.66666603]
  [0.         0.         0.        ]
  [0.         0.         0.        ]]

 [[0.         0.25       0.        ]
  [0.25       0.25       0.25      ]
  [0.         0.         0.        ]
  [0.         0.         0.        ]]]

A:
<code>
import tensorflow as tf

x = [[[[1, 2, 3], [2, 3, 4], [0, 0, 0]],
      [[1, 2, 3], [2, 0, 4], [3, 4, 5]],
      [[1, 2, 3], [0, 0, 0], [0, 0, 0]],
      [[1, 2, 3], [1, 2, 3], [0, 0, 0]]],
     [[[1, 2, 3], [0, 1, 0], [0, 0, 0]],
      [[1, 2, 3], [2, 3, 4], [0, 0, 0]],
      [[1, 2, 3], [0, 0, 0], [0, 0, 0]],
      [[1, 2, 3], [1, 2, 3], [1, 2, 3]]]]
x = tf.convert_to_tensor(x, dtype=tf.float32)
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(x):
    non_zero = tf.cast(x != 0, tf.float32)
    y = tf.reduce_sum(x, axis=-2) / tf.reduce_sum(non_zero, axis=-2)
    y = y * y
    z = tf.reduce_sum(x*x, axis=-2) / tf.reduce_sum(non_zero, axis=-2)
    return z-y

result = g(x.__copy__())


Prompt:
Problem:
I'm using tensorflow 2.10.0.
I've come across a case in which the averaging includes padded values. Given a tensor X of some shape (batch_size, ..., features), there could be zero padded features to get the same shape.
How can I average the second to last dimension of X (the features) but only the non-zero entries? So, we divide by the sum by the number of non-zero entries.
Example input:
x = [[[[1,2,3], [2,3,4], [0,0,0]],
       [[1,2,3], [2,0,4], [3,4,5]],
       [[1,2,3], [0,0,0], [0,0,0]],
       [[1,2,3], [1,2,3], [0,0,0]]],
      [[[1,2,3], [0,1,0], [0,0,0]],
       [[1,2,3], [2,3,4], [0,0,0]],
       [[1,2,3], [0,0,0], [0,0,0]],
       [[1,2,3], [1,2,3], [1,2,3]]]]
# Desired output
y = [[[1.5 2.5 3.5]
      [2.  2.  4. ]
      [1.  2.  3. ]
      [1.  2.  3. ]]
     [[0.5 1.5 1.5]
      [1.5 2.5 3.5]
      [1.  2.  3. ]
      [1.  2.  3. ]]]

A:
<code>
import tensorflow as tf

example_x = [[[[1, 2, 3], [2, 3, 4], [0, 0, 0]],
      [[1, 2, 3], [2, 0, 4], [3, 4, 5]],
      [[1, 2, 3], [0, 0, 0], [0, 0, 0]],
      [[1, 2, 3], [1, 2, 3], [0, 0, 0]]],
     [[[1, 2, 3], [0, 1, 0], [0, 0, 0]],
      [[1, 2, 3], [2, 3, 4], [0, 0, 0]],
      [[1, 2, 3], [0, 0, 0], [0, 0, 0]],
      [[1, 2, 3], [1, 2, 3], [1, 2, 3]]]]
example_x = tf.convert_to_tensor(example_x, dtype=tf.float32)
def f(x=example_x):
    # return the solution in this function
    # result = f(x)
    ### BEGIN SOLUTION

    non_zero = tf.cast(x != 0, tf.float32)
    y = tf.reduce_sum(x, axis=-2) / tf.reduce_sum(non_zero, axis=-2)
    result = y

    return result


Prompt:
Problem:
How would you convert this Tensorflow 1.5 code to Tensorflow 2.3.0?
import tensorflow as tf

try:
    Session = tf.Session
except AttributeError:
    Session = tf.compat.v1.Session
tf.random.set_seed(10)
A = tf.random.normal([100,100])
B = tf.random.normal([100,100])
with Session() as sess:
   result = sess.run(tf.reduce_sum(tf.matmul(A,B)))

The main problem is that the Session class has been removed in Tensorflow 2, and the version exposed in the compat.v1 layer doesn't actually appear to be compatible. When I run this code with Tensorflow 2, it now throws the exception:
RuntimeError: Attempting to capture an EagerTensor without building a function.

If I drop the use of Session entirely, is that still functionally equivalent? If I run:
import tensorflow as tf
A = tf.random.normal([100,100])
B = tf.random.normal([100,100])
with Session() as sess:
    print(tf.reduce_sum(tf.matmul(A,B)))

it runs significantly faster (0.005sec vs 30sec) in Tensoflow 1.16 with AVX2 support, whereas stock Tensorflow 2 installed from pip (without AVX2 support) also runs a bit faster (30sec vs 60sec).
Why would the use of Session slow down Tensorflow 1.16 by 6000x?

A:
<code>
import tensorflow as tf

</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

tf.random.set_seed(10)
def get_values():
  A = tf.random.normal([100,100])
  B = tf.random.normal([100,100])
  return A,B

@tf.function
def compute():
  A,B = get_values()
  return tf.reduce_sum(tf.matmul(A,B))

result = compute()

import tensorflow as tf
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        FLAG = data
        return -805.02057

    def define_test_input(test_case_id):
        if test_case_id == 1:
            FLAG = 114514
        return FLAG

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        assert abs(result - ans) <= 0.02
        return 1
    except:
        return 0

exec_context = r"""
import tensorflow as tf
FLAG = test_input
[insert].numpy()
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
I'm using tensorflow 2.10.0.
So I'm creating a tensorflow model and for the forward pass, I'm applying my forward pass method to get the scores tensor which contains the prediction scores for each class. The shape of this tensor is [100, 10]. Now, I want to get the accuracy by comparing it to y which contains the actual scores. This tensor has the shape [100]. To compare the two I'll be using torch.mean(scores == y) and I'll count how many are the same.
The problem is that I need to convert the scores tensor so that each row simply contains the index of the highest value in each row. For example if the tensor looked like this,
tf.Tensor(
    [[0.3232, -0.2321, 0.2332, -0.1231, 0.2435, 0.6728],
    [0.2323, -0.1231, -0.5321, -0.1452, 0.5435, 0.1722],
    [0.9823, -0.1321, -0.6433, 0.1231, 0.023, 0.0711]]
)

Then I'd want it to be converted so that it looks like this.
tf.Tensor([5 4 0])

How could I do that?

A:
<code>
import tensorflow as tf

a = tf.constant(
    [[0.3232, -0.2321, 0.2332, -0.1231, 0.2435, 0.6728],
     [0.2323, -0.1231, -0.5321, -0.1452, 0.5435, 0.1722],
     [0.9823, -0.1321, -0.6433, 0.1231, 0.023, 0.0711]]
)
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(a):
    return tf.argmax(a,axis=1)

result = g(a.__copy__())

import tensorflow as tf
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        a = data
        return tf.argmax(a, axis=1)

    def define_test_input(test_case_id):
        if test_case_id == 1:
            a = tf.constant(
                [
                    [0.3232, -0.2321, 0.2332, -0.1231, 0.2435, 0.6728],
                    [0.2323, -0.1231, -0.5321, -0.1452, 0.5435, 0.1722],
                    [0.9823, -0.1321, -0.6433, 0.1231, 0.023, 0.0711],
                ]
            )
        if test_case_id == 2:
            a = tf.constant(
                [
                    [0.3232, -0.2321, 0.2332, -0.1231, 0.2435],
                    [0.2323, -0.1231, -0.5321, -0.1452, 0.5435],
                    [0.9823, -0.1321, -0.6433, 0.1231, 0.023],
                ]
            )
        return a

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    def tensor_equal(a, b):
        if type(a) != type(b):
            return False
        if isinstance(a, type(tf.constant([]))) is not True:
            if isinstance(a, type(tf.Variable([]))) is not True:
                return False
        if a.shape != b.shape:
            return False
        if a.dtype != tf.float32:
            a = tf.cast(a, tf.float32)
        if b.dtype != tf.float32:
            b = tf.cast(b, tf.float32)
        if not tf.reduce_min(tf.cast(a == b, dtype=tf.int32)):
            return False
        return True

    try:
        assert tensor_equal(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import tensorflow as tf
a = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
I'm using tensorflow 2.10.0.
So I'm creating a tensorflow model and for the forward pass, I'm applying my forward pass method to get the scores tensor which contains the prediction scores for each class. The shape of this tensor is [100, 10]. Now, I want to get the accuracy by comparing it to y which contains the actual scores. This tensor has the shape [10]. To compare the two I'll be using torch.mean(scores == y) and I'll count how many are the same.
The problem is that I need to convert the scores tensor so that each row simply contains the index of the highest value in each column. For example if the tensor looked like this,
tf.Tensor(
    [[0.3232, -0.2321, 0.2332, -0.1231, 0.2435, 0.6728],
    [0.2323, -0.1231, -0.5321, -0.1452, 0.5435, 0.1722],
    [0.9823, -0.1321, -0.6433, 0.1231, 0.023, 0.0711]]
)

Then I'd want it to be converted so that it looks like this.
tf.Tensor([2 1 0 2 1 0])

How could I do that?

A:
<code>
import tensorflow as tf

a = tf.constant(
    [[0.3232, -0.2321, 0.2332, -0.1231, 0.2435, 0.6728],
     [0.2323, -0.1231, -0.5321, -0.1452, 0.5435, 0.1722],
     [0.9823, -0.1321, -0.6433, 0.1231, 0.023, 0.0711]]
)
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(a):
    return tf.argmax(a,axis=0)

result = g(a.__copy__())

import tensorflow as tf
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        a = data
        return tf.argmax(a, axis=0)

    def define_test_input(test_case_id):
        if test_case_id == 1:
            a = tf.constant(
                [
                    [0.3232, -0.2321, 0.2332, -0.1231, 0.2435, 0.6728],
                    [0.2323, -0.1231, -0.5321, -0.1452, 0.5435, 0.1722],
                    [0.9823, -0.1321, -0.6433, 0.1231, 0.023, 0.0711],
                ]
            )
        if test_case_id == 2:
            a = tf.constant(
                [
                    [0.3232, -0.2321, 0.2332, -0.1231, 0.2435],
                    [0.2323, -0.1231, -0.5321, -0.1452, 0.5435],
                    [0.9823, -0.1321, -0.6433, 0.1231, 0.023],
                ]
            )
        return a

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    def tensor_equal(a, b):
        if type(a) != type(b):
            return False
        if isinstance(a, type(tf.constant([]))) is not True:
            if isinstance(a, type(tf.Variable([]))) is not True:
                return False
        if a.shape != b.shape:
            return False
        if a.dtype != tf.float32:
            a = tf.cast(a, tf.float32)
        if b.dtype != tf.float32:
            b = tf.cast(b, tf.float32)
        if not tf.reduce_min(tf.cast(a == b, dtype=tf.int32)):
            return False
        return True

    try:
        assert tensor_equal(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import tensorflow as tf
a = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
I'm using tensorflow 2.10.0.
So I'm creating a tensorflow model and for the forward pass, I'm applying my forward pass method to get the scores tensor which contains the prediction scores for each class. The shape of this tensor is [100, 10]. Now, I want to get the accuracy by comparing it to y which contains the actual scores. This tensor has the shape [100]. To compare the two I'll be using torch.mean(scores == y) and I'll count how many are the same.
The problem is that I need to convert the scores tensor so that each row simply contains the index of the highest value in each row. For example if the tensor looked like this,
tf.Tensor(
    [[0.3232, -0.2321, 0.2332, -0.1231, 0.2435, 0.6728],
    [0.2323, -0.1231, -0.5321, -0.1452, 0.5435, 0.1722],
    [0.9823, -0.1321, -0.6433, 0.1231, 0.023, 0.0711]]
)

Then I'd want it to be converted so that it looks like this.
tf.Tensor([5 4 0])

How could I do that?

A:
<code>
import tensorflow as tf

example_a = tf.constant(
    [[0.3232, -0.2321, 0.2332, -0.1231, 0.2435, 0.6728],
     [0.2323, -0.1231, -0.5321, -0.1452, 0.5435, 0.1722],
     [0.9823, -0.1321, -0.6433, 0.1231, 0.023, 0.0711]]
)
def f(a=example_a):
    # return the solution in this function
    # result = f(a)
    ### BEGIN SOLUTION

    result = tf.argmax(a,axis=1)

    return result

import tensorflow as tf
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        a = data
        return tf.argmax(a, axis=1)

    def define_test_input(test_case_id):
        if test_case_id == 1:
            a = tf.constant(
                [
                    [0.3232, -0.2321, 0.2332, -0.1231, 0.2435, 0.6728],
                    [0.2323, -0.1231, -0.5321, -0.1452, 0.5435, 0.1722],
                    [0.9823, -0.1321, -0.6433, 0.1231, 0.023, 0.0711],
                ]
            )
        if test_case_id == 2:
            a = tf.constant(
                [
                    [0.3232, -0.2321, 0.2332, -0.1231, 0.2435],
                    [0.2323, -0.1231, -0.5321, -0.1452, 0.5435],
                    [0.9823, -0.1321, -0.6433, 0.1231, 0.023],
                ]
            )
        return a

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    def tensor_equal(a, b):
        if type(a) != type(b):
            return False
        if isinstance(a, type(tf.constant([]))) is not True:
            if isinstance(a, type(tf.Variable([]))) is not True:
                return False
        if a.shape != b.shape:
            return False
        if a.dtype != tf.float32:
            a = tf.cast(a, tf.float32)
        if b.dtype != tf.float32:
            b = tf.cast(b, tf.float32)
        if not tf.reduce_min(tf.cast(a == b, dtype=tf.int32)):
            return False
        return True

    try:
        assert tensor_equal(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import tensorflow as tf
a = test_input
def f(a):
[insert]
result = f(a)
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
I'm using tensorflow 2.10.0.
The problem is that I need to convert the scores tensor so that each row simply contains the index of the lowest value in each column. For example if the tensor looked like this,
tf.Tensor(
    [[0.3232, -0.2321, 0.2332, -0.1231, 0.2435, 0.6728],
    [0.2323, -0.1231, -0.5321, -0.1452, 0.5435, 0.1722],
    [0.9823, -0.1321, -0.6433, 0.1231, 0.023, 0.0711]]
)

Then I'd want it to be converted so that it looks like this.
tf.Tensor([1 0 2 1 2 2])

How could I do that?

A:
<code>
import tensorflow as tf

a = tf.constant(
    [[0.3232, -0.2321, 0.2332, -0.1231, 0.2435, 0.6728],
     [0.2323, -0.1231, -0.5321, -0.1452, 0.5435, 0.1722],
     [0.9823, -0.1321, -0.6433, 0.1231, 0.023, 0.0711]]
)
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(a):
    return tf.argmin(a,axis=0)

result = g(a.__copy__())

import tensorflow as tf
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        a = data
        return tf.argmin(a, axis=0)

    def define_test_input(test_case_id):
        if test_case_id == 1:
            a = tf.constant(
                [
                    [0.3232, -0.2321, 0.2332, -0.1231, 0.2435, 0.6728],
                    [0.2323, -0.1231, -0.5321, -0.1452, 0.5435, 0.1722],
                    [0.9823, -0.1321, -0.6433, 0.1231, 0.023, 0.0711],
                ]
            )
        if test_case_id == 2:
            a = tf.constant(
                [
                    [0.3232, -0.2321, 0.2332, -0.1231, 0.2435],
                    [0.2323, -0.1231, -0.5321, -0.1452, 0.5435],
                    [0.9823, -0.1321, -0.6433, 0.1231, 0.023],
                ]
            )
        return a

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    def tensor_equal(a, b):
        if type(a) != type(b):
            return False
        if isinstance(a, type(tf.constant([]))) is not True:
            if isinstance(a, type(tf.Variable([]))) is not True:
                return False
        if a.shape != b.shape:
            return False
        if a.dtype != tf.float32:
            a = tf.cast(a, tf.float32)
        if b.dtype != tf.float32:
            b = tf.cast(b, tf.float32)
        if not tf.reduce_min(tf.cast(a == b, dtype=tf.int32)):
            return False
        return True

    try:
        assert tensor_equal(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import tensorflow as tf
a = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
I'm using tensorflow 2.10.0.
I am trying to save my ANN model using SavedModel format. The command that I used was:
model.save("my_model")

It supposed to give me a folder namely "my_model" that contains all saved_model.pb, variables and asset, instead it gives me an HDF file namely my_model. I am using keras v.2.3.1 and tensorflow v.2.3.0
Here is a bit of my code:
from keras import optimizers
from keras import backend
from keras.models import Sequential
from keras.layers import Dense
from keras.activations import relu,tanh,sigmoid
network_layout = []
for i in range(3):
    network_layout.append(8)
model = Sequential()
#Adding input layer and first hidden layer
model.add(Dense(network_layout[0],
                name = "Input",
                input_dim=inputdim,
                kernel_initializer='he_normal',
                activation=activation))
#Adding the rest of hidden layer
for numneurons in network_layout[1:]:
    model.add(Dense(numneurons,
                    kernel_initializer = 'he_normal',
                    activation=activation))
#Adding the output layer
model.add(Dense(outputdim,
                name="Output",
                kernel_initializer="he_normal",
                activation="relu"))
#Compiling the model
model.compile(optimizer=opt,loss='mse',metrics=['mse','mae','mape'])
model.summary()
#Training the model
history = model.fit(x=Xtrain,y=ytrain,validation_data=(Xtest,ytest),batch_size=32,epochs=epochs)
model.save('my_model')

I have read the API documentation in the tensorflow website and I did what it said to use model.save("my_model") without any file extension, but I can't get it right.
Your help will be very appreciated. Thanks a bunch!

A:
<code>
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

network_layout = []
for i in range(3):
    network_layout.append(8)

model = Sequential()

inputdim = 4
activation = 'relu'
outputdim = 2
opt='rmsprop'
epochs = 50
#Adding input layer and first hidden layer
model.add(Dense(network_layout[0],
                name="Input",
                input_dim=inputdim,
                kernel_initializer='he_normal',
                activation=activation))

#Adding the rest of hidden layer
for numneurons in network_layout[1:]:
    model.add(Dense(numneurons,
                    kernel_initializer = 'he_normal',
                    activation=activation))

#Adding the output layer
model.add(Dense(outputdim,
                name="Output",
                kernel_initializer="he_normal",
                activation="relu"))

#Compiling the model
model.compile(optimizer=opt,loss='mse',metrics=['mse','mae','mape'])
model.summary()

#Save the model in "export/1"
</code>
BEGIN SOLUTION
<code>

import copy
import tokenize, io

def generate_test_case(test_case_id):
    def generate_ans(data):
        FLAG = data
        return 1

    def define_test_input(test_case_id):
        if test_case_id == 1:
            FLAG = 114514
        return FLAG

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        assert result == ans
        return 1
    except:
        return 0

exec_context = r"""
import shutil
import os
if os.path.exists('my_model'):
    shutil.rmtree('my_model')
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
FLAG = test_input
network_layout = []
for i in range(3):
    network_layout.append(8)
model = Sequential()
inputdim = 4
activation = 'relu'
outputdim = 2
opt='rmsprop'
epochs = 50
model.add(Dense(network_layout[0],
                name="Input",
                input_dim=inputdim,
                kernel_initializer='he_normal',
                activation=activation))
for numneurons in network_layout[1:]:
    model.add(Dense(numneurons,
                    kernel_initializer = 'he_normal',
                    activation=activation))
model.add(Dense(outputdim,
                name="Output",
                kernel_initializer="he_normal",
                activation="relu"))
model.compile(optimizer=opt,loss='mse',metrics=['mse','mae','mape'])
[insert]
try:
    assert os.path.exists("export")
    p = os.path.join("export", "1")
    assert os.path.exists(p)
    assert os.path.exists(os.path.join(p, "assets"))
    assert os.path.exists(os.path.join(p, "saved_model.pb"))
    p = os.path.join(p, "variables")
    assert os.path.exists(p)
    assert os.path.exists(os.path.join(p, "variables.data-00000-of-00001"))
    assert os.path.exists(os.path.join(p, "variables.index"))
    result = 1
except:
    result = 0
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "saved_model" in tokens


Prompt:
Problem:
I'm using tensorflow 2.10.0.
I would like to generate 10 random integers as a tensor in TensorFlow but I don't which command I should use. In particular, I would like to generate from a uniform random variable which takes values in {1, 2, 3, 4}. I have tried to look among the distributions included in tensorflow_probability but I didn't find it.
Please set the random seed to 10 with tf.random.ser_seed().
Thanks in advance for your help.

A:
<code>
import tensorflow as tf

seed_x = 10
### return the tensor as variable 'result'
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(seed_x):
    tf.random.set_seed(seed_x)
    return tf.random.uniform(shape=(10,), minval=1, maxval=5, dtype=tf.int32)

result = g(seed_x)

import tensorflow as tf
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        seed_x = data
        tf.random.set_seed(seed_x)
        return tf.random.uniform(shape=(10,), minval=1, maxval=5, dtype=tf.int32)

    def define_test_input(test_case_id):
        if test_case_id == 1:
            seed_x = 10
        return seed_x

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    def tensor_equal(a, b):
        if type(a) != type(b):
            return False
        if isinstance(a, type(tf.constant([]))) is not True:
            if isinstance(a, type(tf.Variable([]))) is not True:
                return False
        if a.shape != b.shape:
            return False
        if a.dtype != tf.float32:
            a = tf.cast(a, tf.float32)
        if b.dtype != tf.float32:
            b = tf.cast(b, tf.float32)
        if not tf.reduce_min(tf.cast(a == b, dtype=tf.int32)):
            return False
        return True

    try:
        assert tensor_equal(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import tensorflow as tf
seed_x = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
I'm using tensorflow 2.10.0.
I would like to generate 114 random integers as a tensor in TensorFlow but I don't which command I should use. In particular, I would like to generate from a uniform random variable which takes values in {2, 3, 4, 5}. I have tried to look among the distributions included in tensorflow_probability but I didn't find it.
Please set the random seed to seed_x with tf.random.ser_seed().
Thanks in advance for your help.

A:
<code>
import tensorflow as tf

seed_x = 10
### return the tensor as variable 'result'
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(seed_x):
    tf.random.set_seed(seed_x)
    return tf.random.uniform(shape=(114,), minval=2, maxval=6, dtype=tf.int32)

result = g(seed_x)

import tensorflow as tf
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        seed_x = data
        tf.random.set_seed(seed_x)
        return tf.random.uniform(shape=(114,), minval=2, maxval=6, dtype=tf.int32)

    def define_test_input(test_case_id):
        if test_case_id == 1:
            seed_x = 10
        return seed_x

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    def tensor_equal(a, b):
        if type(a) != type(b):
            return False
        if isinstance(a, type(tf.constant([]))) is not True:
            if isinstance(a, type(tf.Variable([]))) is not True:
                return False
        if a.shape != b.shape:
            return False
        if a.dtype != tf.float32:
            a = tf.cast(a, tf.float32)
        if b.dtype != tf.float32:
            b = tf.cast(b, tf.float32)
        if not tf.reduce_min(tf.cast(a == b, dtype=tf.int32)):
            return False
        return True

    try:
        assert tensor_equal(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import tensorflow as tf
seed_x = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
I'm using tensorflow 2.10.0.
I would like to generate 10 random integers as a tensor in TensorFlow but I don't which command I should use. In particular, I would like to generate from a uniform random variable which takes values in {1, 2, 3, 4}. I have tried to look among the distributions included in tensorflow_probability but I didn't find it.
Please set the random seed to 10 with tf.random.ser_seed().
Thanks in advance for your help.

A:
<code>
import tensorflow as tf

def f(seed_x=10):
    # return the solution in this function
    # result = f(seed_x)
    ### BEGIN SOLUTION

    tf.random.set_seed(seed_x)
    result = tf.random.uniform(shape=(10,), minval=1, maxval=5, dtype=tf.int32)

    return result

import tensorflow as tf
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        seed_x = data
        tf.random.set_seed(seed_x)
        return tf.random.uniform(shape=(10,), minval=1, maxval=5, dtype=tf.int32)

    def define_test_input(test_case_id):
        if test_case_id == 1:
            seed_x = 10
        return seed_x

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    def tensor_equal(a, b):
        if type(a) != type(b):
            return False
        if isinstance(a, type(tf.constant([]))) is not True:
            if isinstance(a, type(tf.Variable([]))) is not True:
                return False
        if a.shape != b.shape:
            return False
        if a.dtype != tf.float32:
            a = tf.cast(a, tf.float32)
        if b.dtype != tf.float32:
            b = tf.cast(b, tf.float32)
        if not tf.reduce_min(tf.cast(a == b, dtype=tf.int32)):
            return False
        return True

    try:
        assert tensor_equal(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import tensorflow as tf
seed_x = test_input
def f(seed_x):
[insert]
result = f(seed_x)
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
I'm using tensorflow 2.10.0.
I need to find which version of TensorFlow I have installed. I'm using Ubuntu 16.04 Long Term Support.

A:
<code>
import tensorflow as tf

### output the version of tensorflow into variable 'result'
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

import tensorflow as tf
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        FLAG = data
        return tf.version.VERSION

    def define_test_input(test_case_id):
        if test_case_id == 1:
            FLAG = 114514
        return FLAG

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        assert result == ans
        return 1
    except:
        return 0

exec_context = r"""
import tensorflow as tf
FLAG = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
I have a set of data and I want to compare which line describes it best (polynomials of different orders, exponential or logarithmic).
I use Python and Numpy and for polynomial fitting there is a function polyfit().
How do I fit y = Alogx + B using polyfit()? The result should be an np.array of [A, B]
A:
<code>
import numpy as np
import scipy
x = np.array([1, 7, 20, 50, 79])
y = np.array([10, 19, 30, 35, 51])

</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

import numpy as np
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            x = np.array([1, 7, 20, 50, 79])
            y = np.array([10, 19, 30, 35, 51])
        return x, y

    def generate_ans(data):
        _a = data
        x, y = _a
        result = np.polyfit(np.log(x), y, 1)
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    assert np.allclose(result, ans)
    return 1

exec_context = r"""
import numpy as np
import scipy
x, y = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
I have a set of data and I want to compare which line describes it best (polynomials of different orders, exponential or logarithmic).
I use Python and Numpy and for polynomial fitting there is a function polyfit().
How do I fit y = A + Blogx using polyfit()? The result should be an np.array of [A, B]
A:
<code>
import numpy as np
import scipy
x = np.array([1, 7, 20, 50, 79])
y = np.array([10, 19, 30, 35, 51])

</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

import numpy as np
import copy

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            x = np.array([1, 7, 20, 50, 79])
            y = np.array([10, 19, 30, 35, 51])
        return x, y

    def generate_ans(data):
        _a = data
        x, y = _a
        result = np.polyfit(np.log(x), y, 1)[::-1]
        return result

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    assert np.allclose(result, ans)
    return 1

exec_context = r"""
import numpy as np
import scipy
x, y = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)