forward

Prompt:
Implement the Python class `MultiheadAttentionContainer` described below.

Class description:
Implement the MultiheadAttentionContainer class.

Method signatures and docstrings:
- def __init__(self, nhead, in_proj_container, attention_layer, out_proj, batch_first=False) -> None: A multi-head attention container Args: nhead: the number of heads in the multiheadattention model in_proj_container: A container of multi-head in-projection linear layers (a.k.a nn.Linear). attention_layer: The custom attention layer. The input sent from MHA container to the attention layer is in the shape of `(..., L, N * H, E / H)` for query and `(..., S, N * H, E / H)` for key/value while the output shape of the attention layer is expected to be `(..., L, N * H, E / H)`. The attention_layer needs to support broadcast if users want the overall MultiheadAttentionContainer with broadcast. out_proj: The multi-head out-projection layer (a.k.a nn.Linear). batch_first: If ``True``, then the inpu
- def forward(self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, attn_mask: Optional[torch.Tensor]=None, bias_k: Optional[torch.Tensor]=None, bias_v: Optional[torch.Tensor]=None) -> Tuple[torch.Tensor, torch.Tensor]: Args: query (Tensor): The query of the attention function. See "Attention Is All You Need" for more details. key (Tensor): The keys of the attention function. See "Attention Is All You Need" for more details. value (Tensor): The values of the attention function. See "Attention Is All You Need" for more details. attn_mask (BoolTensor, optional): 3D mask that prevents attention to certain positions. bias_k (Tensor, optional): one more key and value sequence to be added to keys at sequence dim (dim=-3). Those are used for incremental decoding. Users should provide ``bias_v``. bias_v (Tensor, optional): one more key and value sequence to be added to values at sequence dim (dim=-3). Those are use

Implement the Python class `MultiheadAttentionContainer` described below.

Class description:
Implement the MultiheadAttentionContainer class.

Method signatures and docstrings:
- def __init__(self, nhead, in_proj_container, attention_layer, out_proj, batch_first=False) -> None: A multi-head attention container Args: nhead: the number of heads in the multiheadattention model in_proj_container: A container of multi-head in-projection linear layers (a.k.a nn.Linear). attention_layer: The custom attention layer. The input sent from MHA container to the attention layer is in the shape of `(..., L, N * H, E / H)` for query and `(..., S, N * H, E / H)` for key/value while the output shape of the attention layer is expected to be `(..., L, N * H, E / H)`. The attention_layer needs to support broadcast if users want the overall MultiheadAttentionContainer with broadcast. out_proj: The multi-head out-projection layer (a.k.a nn.Linear). batch_first: If ``True``, then the inpu
- def forward(self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, attn_mask: Optional[torch.Tensor]=None, bias_k: Optional[torch.Tensor]=None, bias_v: Optional[torch.Tensor]=None) -> Tuple[torch.Tensor, torch.Tensor]: Args: query (Tensor): The query of the attention function. See "Attention Is All You Need" for more details. key (Tensor): The keys of the attention function. See "Attention Is All You Need" for more details. value (Tensor): The values of the attention function. See "Attention Is All You Need" for more details. attn_mask (BoolTensor, optional): 3D mask that prevents attention to certain positions. bias_k (Tensor, optional): one more key and value sequence to be added to keys at sequence dim (dim=-3). Those are used for incremental decoding. Users should provide ``bias_v``. bias_v (Tensor, optional): one more key and value sequence to be added to values at sequence dim (dim=-3). Those are use

<|skeleton|>
class MultiheadAttentionContainer:

    def __init__(self, nhead, in_proj_container, attention_layer, out_proj, batch_first=False) -> None:
        """A multi-head attention container Args: nhead: the number of heads in the multiheadattention model in_proj_container: A container of multi-head in-projection linear layers (a.k.a nn.Linear). attention_layer: The custom attention layer. The input sent from MHA container to the attention layer is in the shape of `(..., L, N * H, E / H)` for query and `(..., S, N * H, E / H)` for key/value while the output shape of the attention layer is expected to be `(..., L, N * H, E / H)`. The attention_layer needs to support broadcast if users want the overall MultiheadAttentionContainer with broadcast. out_proj: The multi-head out-projection layer (a.k.a nn.Linear). batch_first: If ``True``, then the inpu"""
        <|body_0|>

    def forward(self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, attn_mask: Optional[torch.Tensor]=None, bias_k: Optional[torch.Tensor]=None, bias_v: Optional[torch.Tensor]=None) -> Tuple[torch.Tensor, torch.Tensor]:
        """Args: query (Tensor): The query of the attention function. See "Attention Is All You Need" for more details. key (Tensor): The keys of the attention function. See "Attention Is All You Need" for more details. value (Tensor): The values of the attention function. See "Attention Is All You Need" for more details. attn_mask (BoolTensor, optional): 3D mask that prevents attention to certain positions. bias_k (Tensor, optional): one more key and value sequence to be added to keys at sequence dim (dim=-3). Those are used for incremental decoding. Users should provide ``bias_v``. bias_v (Tensor, optional): one more key and value sequence to be added to values at sequence dim (dim=-3). Those are use"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super(MultiheadAttentionContainer, self).__init__()
        self.nhead = nhead
        self.in_proj_container = in_proj_container
        self.attention_layer = attention_layer
        self.out_proj = out_proj
        self.batch_first = batch_first
<|end_body_0|>

<|body_start_1|>
        if self.batch_first:
            query, key, value = (query.transpose(-3, -2), key.transpose(-3, -2), value.transpose(-3, -2))
        tgt_len, src_len, bsz, embed_dim = (query.size(-3), key.size(-3), query.size(-2), query.size(-1))
        q, k, v = self.in_proj_container(query, key, value)
        assert q.size(-1) % self.nhead == 0, "query's embed_dim must be divisible by the number of heads"
        head_dim = q.size(-1) // self.nhead
        q = q.reshape(tgt_len, bsz * self.nhead, head_dim)
        assert k.size(-1) % self.nhead == 0, "key's embed_dim must be divisible by the number of heads"
        head_dim = k.size(-1) // self.nhead
        k = k.reshape(src_len, bsz * self.nhead, head_dim)
        assert v.size(-1) % self.nhead == 0, "value's embed_dim must be divisible by the number of heads"
        head_dim = v.size(-1) // self.nhead
        v = v.reshape(src_len, bsz * self.nhead, head_dim)
        attn_output, attn_output_weights = self.attention_layer(q, k, v, attn_mask=attn_mask, bias_k=bias_k, bias_v=bias_v)
        attn_output = attn_output.reshape(tgt_len, bsz, embed_dim)
        attn_output = self.out_proj(attn_output)
        if self.batch_first:
            attn_output = attn_output.transpose(-3, -2)
        return (attn_output, attn_output_weights)
<|end_body_1|>

Solution:
class MultiheadAttentionContainer:
    def __init__(self, nhead, in_proj_container, attention_layer, out_proj, batch_first=False) -> None:
        """A multi-head attention container Args: nhead: the number of heads in the multiheadattention model in_proj_container: A container of multi-head in-projection linear layers (a.k.a nn.Linear). attention_layer: The custom attention layer. The input sent from MHA container to the attention layer is in the shape of `(..., L, N * H, E / H)` for query and `(..., S, N * H, E / H)` for key/value while the output shape of the attention layer is expected to be `(..., L, N * H, E / H)`. The attention_layer needs to support broadcast if users want the overall MultiheadAttentionContainer with broadcast. out_proj: The multi-head out-projection layer (a.k.a nn.Linear). batch_first: If ``True``, then the inpu"""
        super(MultiheadAttentionContainer, self).__init__()
        self.nhead = nhead
        self.in_proj_container = in_proj_container
        self.attention_layer = attention_layer
        self.out_proj = out_proj
        self.batch_first = batch_first

    def forward(self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, attn_mask: Optional[torch.Tensor]=None, bias_k: Optional[torch.Tensor]=None, bias_v: Optional[torch.Tensor]=None) -> Tuple[torch.Tensor, torch.Tensor]:
        """Args: query (Tensor): The query of the attention function. See "Attention Is All You Need" for more details. key (Tensor): The keys of the attention function. See "Attention Is All You Need" for more details. value (Tensor): The values of the attention function. See "Attention Is All You Need" for more details. attn_mask (BoolTensor, optional): 3D mask that prevents attention to certain positions. bias_k (Tensor, optional): one more key and value sequence to be added to keys at sequence dim (dim=-3). Those are used for incremental decoding. Users should provide ``bias_v``. bias_v (Tensor, optional): one more key and value sequence to be added to values at sequence dim (dim=-3). Those are use"""
        if self.batch_first:
            query, key, value = (query.transpose(-3, -2), key.transpose(-3, -2), value.transpose(-3, -2))
        tgt_len, src_len, bsz, embed_dim = (query.size(-3), key.size(-3), query.size(-2), query.size(-1))
        q, k, v = self.in_proj_container(query, key, value)
        assert q.size(-1) % self.nhead == 0, "query's embed_dim must be divisible by the number of heads"
        head_dim = q.size(-1) // self.nhead
        q = q.reshape(tgt_len, bsz * self.nhead, head_dim)
        assert k.size(-1) % self.nhead == 0, "key's embed_dim must be divisible by the number of heads"
        head_dim = k.size(-1) // self.nhead
        k = k.reshape(src_len, bsz * self.nhead, head_dim)
        assert v.size(-1) % self.nhead == 0, "value's embed_dim must be divisible by the number of heads"
        head_dim = v.size(-1) // self.nhead
        v = v.reshape(src_len, bsz * self.nhead, head_dim)
        attn_output, attn_output_weights = self.attention_layer(q, k, v, attn_mask=attn_mask, bias_k=bias_k, bias_v=bias_v)
        attn_output = attn_output.reshape(tgt_len, bsz, embed_dim)
        attn_output = self.out_proj(attn_output)
        if self.batch_first:
            attn_output = attn_output.transpose(-3, -2)
        return (attn_output, attn_output_weights)


wordBreak

Prompt:
Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def wordBreak(self, s, wordDict): :type s: str :type wordDict: Set[str] :rtype: bool
- def word_break_aux(self, s, wordDict, i, t): Determine if s[:i + 1] can be segmented by dict wordDict

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def wordBreak(self, s, wordDict): :type s: str :type wordDict: Set[str] :rtype: bool
- def word_break_aux(self, s, wordDict, i, t): Determine if s[:i + 1] can be segmented by dict wordDict

<|skeleton|>
class Solution:

    def wordBreak(self, s, wordDict):
        """:type s: str :type wordDict: Set[str] :rtype: bool"""
        <|body_0|>

    def word_break_aux(self, s, wordDict, i, t):
        """Determine if s[:i + 1] can be segmented by dict wordDict"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        n = len(s)
        t = [None for i in range(n)]
        return self.word_break_aux(s, wordDict, n - 1, t)
<|end_body_0|>

<|body_start_1|>
        if s[:i + 1] in wordDict:
            return True
        elif t[i] is not None:
            return t[i]
        else:
            for j in range(i):
                if self.word_break_aux(s, wordDict, j, t) is True and s[j + 1:i + 1] in wordDict:
                    t[i] = True
                    return True
            else:
                t[i] = False
                return False
<|end_body_1|>

Solution:
class Solution:
    def wordBreak(self, s, wordDict):
        """:type s: str :type wordDict: Set[str] :rtype: bool"""
        n = len(s)
        t = [None for i in range(n)]
        return self.word_break_aux(s, wordDict, n - 1, t)

    def word_break_aux(self, s, wordDict, i, t):
        """Determine if s[:i + 1] can be segmented by dict wordDict"""
        if s[:i + 1] in wordDict:
            return True
        elif t[i] is not None:
            return t[i]
        else:
            for j in range(i):
                if self.word_break_aux(s, wordDict, j, t) is True and s[j + 1:i + 1] in wordDict:
                    t[i] = True
                    return True
            else:
                t[i] = False
                return False


maxProduct

Prompt:
Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def maxProduct(self, nums): 使用动规，也就是 :param nums: :return:
- def maxProduct(self, nums): 使用正序和倒叙 两个计算方式，其实是使用 :param nums: :return:
- def maxProduct(self, nums): 为什么最后是加号？？？？ :param nums: :return:

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def maxProduct(self, nums): 使用动规，也就是 :param nums: :return:
- def maxProduct(self, nums): 使用正序和倒叙 两个计算方式，其实是使用 :param nums: :return:
- def maxProduct(self, nums): 为什么最后是加号？？？？ :param nums: :return:

<|skeleton|>
class Solution:

    def maxProduct(self, nums):
        """使用动规，也就是 :param nums: :return:"""
        <|body_0|>

    def maxProduct(self, nums):
        """使用正序和倒叙 两个计算方式，其实是使用 :param nums: :return:"""
        <|body_1|>

    def maxProduct(self, nums):
        """为什么最后是加号？？？？ :param nums: :return:"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        N = len(nums)
        if N == 1:
            return nums[0]
        dpmax = [0 for i in range(N)]
        dpmin = [0 for i in range(N)]
        dpmax[0] = nums[0]
        dpmin[0] = nums[0]
        res = nums[0]
        for i in range(1, N):
            dpmax[i] = max(dpmax[i - 1] * nums[i], dpmin[i - 1] * nums[i], nums[i])
            dpmin[i] = min(dpmax[i - 1] * nums[i], dpmin[i - 1] * nums[i], nums[i])
            res = max(res, dpmax[i])
        return res
<|end_body_0|>

<|body_start_1|>
        N = len(nums)
        numsTemp = nums[::-1]
        res = nums[0]
        tmp = nums[0]
        for i in range(1, N):
            if nums[i - 1] == 0:
                tmp = nums[i]
            else:
                tmp *= nums[i]
            res = max(tmp, res)
        tmp = numsTemp[0]
        res2 = numsTemp[0]
        for i in range(1, N):
            if numsTemp[i - 1] == 0:
                tmp = numsTemp[i]
            else:
                tmp *= numsTemp[i]
            res2 = max(tmp, res2)
        return max(res, res2)
<|end_body_1|>

<|body_start_2|>
        reverse_nums = nums[::-1]
        for i in range(1, len(nums)):
            nums[i] *= nums[i - 1] or 1
            reverse_nums[i] *= reverse_nums[i - 1] or 1
        return max(nums + reverse_nums)
<|end_body_2|>

Solution:
class Solution:
    def maxProduct(self, nums):
        """使用动规，也就是 :param nums: :return:"""
        N = len(nums)
        if N == 1:
            return nums[0]
        dpmax = [0 for i in range(N)]
        dpmin = [0 for i in range(N)]
        dpmax[0] = nums[0]
        dpmin[0] = nums[0]
        res = nums[0]
        for i in range(1, N):
            dpmax[i] = max(dpmax[i - 1] * nums[i], dpmin[i - 1] * nums[i], nums[i])
            dpmin[i] = min(dpmax[i - 1] * nums[i], dpmin[i - 1] * nums[i], nums[i])
            res = max(res, dpmax[i])
        return res

    def maxProduct(self, nums):
        """使用正序和倒叙 两个计算方式，其实是使用 :param nums: :return:"""
        N = len(nums)
        numsTemp = nums[::-1]
        res = nums[0]
        tmp = nums[0]
        for i in range(1, N):
            if nums[i - 1] == 0:
                tmp = nums[i]
            else:
                tmp *= nums[i]
            res = max(tmp, res)
        tmp = numsTemp[0]
        res2 = numsTemp[0]
        for i in range(1, N):
            if numsTemp[i - 1] == 0:
                tmp = numsTemp[i]
            else:
                tmp *= numsTemp[i]
            res2 = max(tmp, res2)
        return max(res, res2)

    def maxProduct(self, nums):
        """为什么最后是加号？？？？ :param nums: :return:"""
        reverse_nums = nums[::-1]
        for i in range(1, len(nums)):
            nums[i] *= nums[i - 1] or 1
            reverse_nums[i] *= reverse_nums[i - 1] or 1
        return max(nums + reverse_nums)


Prompt:
Implement the Python class `Menu` described below.

Class description:
Implement the Menu class.

Method signatures and docstrings:
- def __init__(self, game): Initialize game menu
- def border(self): Create court border
- def play(self): Create play button
- def pause(self): Create pause button
- def click(self, x, y): Assign menu function on mouse click

Implement the Python class `Menu` described below.

Class description:
Implement the Menu class.

Method signatures and docstrings:
- def __init__(self, game): Initialize game menu
- def border(self): Create court border
- def play(self): Create play button
- def pause(self): Create pause button
- def click(self, x, y): Assign menu function on mouse click

<|skeleton|>
class Menu:

    def __init__(self, game):
        """Initialize game menu"""
        <|body_0|>

    def border(self):
        """Create court border"""
        <|body_1|>

    def play(self):
        """Create play button"""
        <|body_2|>

    def pause(self):
        """Create pause button"""
        <|body_3|>

    def click(self, x, y):
        """Assign menu function on mouse click"""
        <|body_4|>

<|end_skeleton|>

<|body_start_0|>
        super().__init__(visible=False)
        self.color('white')
        self.shape('square')
        self.setheading(90)
        self.shapesize(0.1, 1)
        self.speed(0)
        self.penup()
        self.pensize(3)
        self.game = game
        self.court = self.game.court
        self.court.onscreenclick(self.click)
        self.font_size = None
<|end_body_0|>

<|body_start_1|>
        self.penup()
        self.goto(-self.court.width, -self.court.height / 2)
        self.pendown()
        self.goto(self.court.width, -self.court.height / 2)
        self.penup()
        self.goto(-self.court.width, self.court.height / 2 - self.court.score_height)
        self.pendown()
        self.goto(self.court.width, self.court.height / 2 - self.court.score_height)
        self.penup()
        self.goto(0, self.court.height / 2 - self.court.score_height)
        self.pendown()
        self.goto(0, -self.court.height / 2)
        self.penup()
<|end_body_1|>

<|body_start_2|>
        self.font_size = int(self.court.score_height / 4)
        self.goto(-self.court.width / 2, self.court.height / 2 - self.court.score_height / 2)
        self.write('Play', font=('Monospace', self.font_size, 'bold'))
<|end_body_2|>

<|body_start_3|>
        self.goto(-5, self.court.height / 2 - self.court.score_height / 2)
        self.stamp()
        self.goto(5, self.court.height / 2 - self.court.score_height / 2)
        self.stamp()
<|end_body_3|>

<|body_start_4|>
        print(x, y)
        if -self.court.width / 2 <= x <= -self.court.width / 2 + self.court.width / 2 / 10 and self.court.height / 2 - self.court.score_height / 2 <= y <= self.court.height / 2:
            self.goto(-self.court.width / 2, self.court.height / 2 - self.court.score_height / 2)
            self.color('green')
            self.write('Play', font=('Monospace', self.font_size, 'bold'))
            self.game.reset()
            self.game.pause_state = False
            self.game.playing()
            self.game.ball.moving = True
            if self.game.humans == 1:
                self.game.p2.cpu()
            elif self.game.humans == 0:
                self.game.p1.cpu()
                self.game.p2.cpu()
            self.undo()
        if -5 <= x <= 5 and self.court.height / 2 - self.court.score_height <= y <= self.court.height / 2:
            self.game.pause()
<|end_body_4|>

Solution:
class Menu:
    def __init__(self, game):
        """Initialize game menu"""
        super().__init__(visible=False)
        self.color('white')
        self.shape('square')
        self.setheading(90)
        self.shapesize(0.1, 1)
        self.speed(0)
        self.penup()
        self.pensize(3)
        self.game = game
        self.court = self.game.court
        self.court.onscreenclick(self.click)
        self.font_size = None

    def border(self):
        """Create court border"""
        self.penup()
        self.goto(-self.court.width, -self.court.height / 2)
        self.pendown()
        self.goto(self.court.width, -self.court.height / 2)
        self.penup()
        self.goto(-self.court.width, self.court.height / 2 - self.court.score_height)
        self.pendown()
        self.goto(self.court.width, self.court.height / 2 - self.court.score_height)
        self.penup()
        self.goto(0, self.court.height / 2 - self.court.score_height)
        self.pendown()
        self.goto(0, -self.court.height / 2)
        self.penup()

    def play(self):
        """Create play button"""
        self.font_size = int(self.court.score_height / 4)
        self.goto(-self.court.width / 2, self.court.height / 2 - self.court.score_height / 2)
        self.write('Play', font=('Monospace', self.font_size, 'bold'))

    def pause(self):
        """Create pause button"""
        self.goto(-5, self.court.height / 2 - self.court.score_height / 2)
        self.stamp()
        self.goto(5, self.court.height / 2 - self.court.score_height / 2)
        self.stamp()

    def click(self, x, y):
        """Assign menu function on mouse click"""
        print(x, y)
        if -self.court.width / 2 <= x <= -self.court.width / 2 + self.court.width / 2 / 10 and self.court.height / 2 - self.court.score_height / 2 <= y <= self.court.height / 2:
            self.goto(-self.court.width / 2, self.court.height / 2 - self.court.score_height / 2)
            self.color('green')
            self.write('Play', font=('Monospace', self.font_size, 'bold'))
            self.game.reset()
            self.game.pause_state = False
            self.game.playing()
            self.game.ball.moving = True
            if self.game.humans == 1:
                self.game.p2.cpu()
            elif self.game.humans == 0:
                self.game.p1.cpu()
                self.game.p2.cpu()
            self.undo()
        if -5 <= x <= 5 and self.court.height / 2 - self.court.score_height <= y <= self.court.height / 2:
            self.game.pause()


Prompt:
Implement the Python class `LoadOverlayAction` described below.

Class description:
The ``LoadOverlayAction`` allows the user to add files to the :class:`.OverlayList`.

Method signatures and docstrings:
- def __init__(self, overlayList, displayCtx, frame): Create a ``LoadOverlayAction``. :arg overlayList: The :class:`.OverlayList`. :arg displayCtx: The :class:`.DisplayContext`. :arg frame: The :class:`.FSLeyesFrame`.
- def __loadOverlay(self): Calls :func:`interactiveLoadOverlays`. If overlays were added, updates the :attr:`.DisplayContext.selectedOverlay` accordingly. If :attr:`.DisplayContext.autoDisplay` is ``True``, uses the :mod:`.autodisplay` module to configure the display properties of each new overlay.

Implement the Python class `LoadOverlayAction` described below.

Class description:
The ``LoadOverlayAction`` allows the user to add files to the :class:`.OverlayList`.

Method signatures and docstrings:
- def __init__(self, overlayList, displayCtx, frame): Create a ``LoadOverlayAction``. :arg overlayList: The :class:`.OverlayList`. :arg displayCtx: The :class:`.DisplayContext`. :arg frame: The :class:`.FSLeyesFrame`.
- def __loadOverlay(self): Calls :func:`interactiveLoadOverlays`. If overlays were added, updates the :attr:`.DisplayContext.selectedOverlay` accordingly. If :attr:`.DisplayContext.autoDisplay` is ``True``, uses the :mod:`.autodisplay` module to configure the display properties of each new overlay.

<|skeleton|>
class LoadOverlayAction:
    """The ``LoadOverlayAction`` allows the user to add files to the :class:`.OverlayList`."""

    def __init__(self, overlayList, displayCtx, frame):
        """Create a ``LoadOverlayAction``. :arg overlayList: The :class:`.OverlayList`. :arg displayCtx: The :class:`.DisplayContext`. :arg frame: The :class:`.FSLeyesFrame`."""
        <|body_0|>

    def __loadOverlay(self):
        """Calls :func:`interactiveLoadOverlays`. If overlays were added, updates the :attr:`.DisplayContext.selectedOverlay` accordingly. If :attr:`.DisplayContext.autoDisplay` is ``True``, uses the :mod:`.autodisplay` module to configure the display properties of each new overlay."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        base.Action.__init__(self, self.__loadOverlay)
        self.__overlayList = overlayList
        self.__displayCtx = displayCtx
<|end_body_0|>

<|body_start_1|>
        def onLoad(paths, overlays):
            if len(overlays) == 0:
                return
            self.__overlayList.extend(overlays)
            self.__displayCtx.selectedOverlay = self.__displayCtx.overlayOrder[-1]
            if self.__displayCtx.autoDisplay:
                for overlay in overlays:
                    autodisplay.autoDisplay(overlay, self.__overlayList, self.__displayCtx)
        interactiveLoadOverlays(onLoad=onLoad, inmem=self.__displayCtx.loadInMemory)
<|end_body_1|>

Solution:
class LoadOverlayAction:
    """The ``LoadOverlayAction`` allows the user to add files to the :class:`.OverlayList`."""

    def __init__(self, overlayList, displayCtx, frame):
        """Create a ``LoadOverlayAction``. :arg overlayList: The :class:`.OverlayList`. :arg displayCtx: The :class:`.DisplayContext`. :arg frame: The :class:`.FSLeyesFrame`."""
        base.Action.__init__(self, self.__loadOverlay)
        self.__overlayList = overlayList
        self.__displayCtx = displayCtx

    def __loadOverlay(self):
        """Calls :func:`interactiveLoadOverlays`. If overlays were added, updates the :attr:`.DisplayContext.selectedOverlay` accordingly. If :attr:`.DisplayContext.autoDisplay` is ``True``, uses the :mod:`.autodisplay` module to configure the display properties of each new overlay."""
        def onLoad(paths, overlays):
            if len(overlays) == 0:
                return
            self.__overlayList.extend(overlays)
            self.__displayCtx.selectedOverlay = self.__displayCtx.overlayOrder[-1]
            if self.__displayCtx.autoDisplay:
                for overlay in overlays:
                    autodisplay.autoDisplay(overlay, self.__overlayList, self.__displayCtx)
        interactiveLoadOverlays(onLoad=onLoad, inmem=self.__displayCtx.loadInMemory)


Prompt:
Implement the Python class `Rankings` described below.

Class description:
Tests the API call for getting top n ranking players

Method signatures and docstrings:
- def test_invalid_api_request(self): Invalid API request. Too many or too few players requested
- def test_top_n(self): Test getting top n players

Implement the Python class `Rankings` described below.

Class description:
Tests the API call for getting top n ranking players

Method signatures and docstrings:
- def test_invalid_api_request(self): Invalid API request. Too many or too few players requested
- def test_top_n(self): Test getting top n players

<|skeleton|>
class Rankings:
    """Tests the API call for getting top n ranking players"""

    def test_invalid_api_request(self):
        """Invalid API request. Too many or too few players requested"""
        <|body_0|>

    def test_top_n(self):
        """Test getting top n players"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        response = self.client.get('/api/rankings/55')
        self.assertEqual(response.status_code, 400, msg=f'{BColors.FAIL}\t[-]\tResponse was not 400 for too large numbers!{BColors.ENDC}')
        print(f'{BColors.OKGREEN}\t[+]\tPass returning the correct response code for too many players.{BColors.ENDC}')
        response2 = self.client.get('/api/rankings/-1')
        self.assertEqual(response2.status_code, 400, msg=f'{BColors.FAIL}\t[-]\tResponse was not 400 for negative numers!{BColors.ENDC}')
        print(f'{BColors.OKGREEN}\t[+]\tPass returning the correct response code for negative number of players.{BColors.ENDC}')
<|end_body_0|>

<|body_start_1|>
        response = self.client.get('/api/rankings/2')
        self.assertEqual(response.status_code, 200, msg=f'{BColors.FAIL}\t[-]\tResponse was not 200!{BColors.ENDC}')
        print(f'{BColors.OKGREEN}\t[+]\tPass returning correct response code for requesting top n players.{BColors.ENDC}')
        top_n_players = response.data['top_ranking_players']
        n = response.data['n']
        self.assertEqual(n, 2, msg=f'{BColors.FAIL}\t[-]\tWrong number of players returned!{BColors.ENDC}')
        print(f'{BColors.OKGREEN}\t[+]\tPass returning correct number of players{BColors.ENDC}')
        first_player = top_n_players[0]['points']['llistStandardPoints']['llistSurvivalPoints']
        second_player = top_n_players[1]['points']['llistStandardPoints']['llistSurvivalPoints']
        self.assertEquals(first_player >= second_player, True, msg=f'{BColors.FAIL}\t[-]\tWrong ordering!{BColors.ENDC}')
        print(f'{BColors.OKGREEN}\t[+]\tPass returning correct ordering of players{BColors.ENDC}')
<|end_body_1|>

Solution:
class Rankings:
    """Tests the API call for getting top n ranking players"""

    def test_invalid_api_request(self):
        """Invalid API request. Too many or too few players requested"""
        response = self.client.get('/api/rankings/55')
        self.assertEqual(response.status_code, 400, msg=f'{BColors.FAIL}\t[-]\tResponse was not 400 for too large numbers!{BColors.ENDC}')
        print(f'{BColors.OKGREEN}\t[+]\tPass returning the correct response code for too many players.{BColors.ENDC}')
        response2 = self.client.get('/api/rankings/-1')
        self.assertEqual(response2.status_code, 400, msg=f'{BColors.FAIL}\t[-]\tResponse was not 400 for negative numers!{BColors.ENDC}')
        print(f'{BColors.OKGREEN}\t[+]\tPass returning the correct response code for negative number of players.{BColors.ENDC}')

    def test_top_n(self):
        """Test getting top n players"""
        response = self.client.get('/api/rankings/2')
        self.assertEqual(response.status_code, 200, msg=f'{BColors.FAIL}\t[-]\tResponse was not 200!{BColors.ENDC}')
        print(f'{BColors.OKGREEN}\t[+]\tPass returning correct response code for requesting top n players.{BColors.ENDC}')
        top_n_players = response.data['top_ranking_players']
        n = response.data['n']
        self.assertEqual(n, 2, msg=f'{BColors.FAIL}\t[-]\tWrong number of players returned!{BColors.ENDC}')
        print(f'{BColors.OKGREEN}\t[+]\tPass returning correct number of players{BColors.ENDC}')
        first_player = top_n_players[0]['points']['llistStandardPoints']['llistSurvivalPoints']
        second_player = top_n_players[1]['points']['llistStandardPoints']['llistSurvivalPoints']
        self.assertEquals(first_player >= second_player, True, msg=f'{BColors.FAIL}\t[-]\tWrong ordering!{BColors.ENDC}')
        print(f'{BColors.OKGREEN}\t[+]\tPass returning correct ordering of players{BColors.ENDC}')


Prompt:
Implement the Python class `RLEIterator` described below.

Class description:
Implement the RLEIterator class.

Method signatures and docstrings:
- def __init__(self, A): :type A: List[int]
- def next(self, n): :type n: int :rtype: int

Implement the Python class `RLEIterator` described below.

Class description:
Implement the RLEIterator class.

Method signatures and docstrings:
- def __init__(self, A): :type A: List[int]
- def next(self, n): :type n: int :rtype: int

<|skeleton|>
class RLEIterator:

    def __init__(self, A):
        """:type A: List[int]"""
        <|body_0|>

    def next(self, n):
        """:type n: int :rtype: int"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.d = []
        for i in range(1, len(A), 2):
            for j in range(A[i - 1]):
                self.d.append(A[i])
<|end_body_0|>

<|body_start_1|>
        if len(self.d) >= n:
            ret = self.d[n - 1]
            self.d = self.d[n:]
        else:
            ret = -1
            self.d = []
        return ret
<|end_body_1|>

Solution:
class RLEIterator:
    def __init__(self, A):
        """:type A: List[int]"""
        self.d = []
        for i in range(1, len(A), 2):
            for j in range(A[i - 1]):
                self.d.append(A[i])

    def next(self, n):
        """:type n: int :rtype: int"""
        if len(self.d) >= n:
            ret = self.d[n - 1]
            self.d = self.d[n:]
        else:
            ret = -1
            self.d = []
        return ret


Prompt:
Implement the Python class `CombinedModel` described below.

Class description:
Model class that combines different types of models -- such as `TriggerChannelModel`, `ActionChannelModel`, and `EnsembledModel` -- and exposes methods to enable them to be evaluated or used on the same set of examples or inputs. Attributes: use_trigger_channel_model (bool): Set to `True` if the trained `TriggerChannelModel` is to be included in the cocktail of models. use_action_channel_model (bool): Set to `True` if the trained `ActionChannelModel` is to be included in the cocktail of models. use_action_fn_model (bool): Set to `True` if the trained `ActionFunctionModel` is to be included in the cocktail of models. use_trigger_fn_model (bool): Set to `True` if the trained `TriggerFunctionMo

Method signatures and docstrings:
- def __init__(self, use_trigger_channel_model=True, use_action_channel_model=True, use_trigger_fn_model=True, use_action_fn_model=True): Sets which types of models to include in the cocktail of models to be used together. Args: use_trigger_channel_model (bool): Add an ensemble of `TriggerChannelModel` models to the cocktail of models if `True`. Defaults to `True`. use_action_channel_model (bool): Add an ensemble of `ActionChannelModel` models to the cocktail of models if `True`. Defaults to `True`.: use_trigger_fn_model (bool): Add an ensemble of `TriggerFunctionModel` models to the cocktail of models if `True`. Defaults to `True`.: use_action_fn_model (bool): Add an ensemble of `ActionFunctionModel` models to the cocktail of models if `True`. Defaults to `True`.:
- def test_models(self): Evaluates the trained models on a common set of test examples. The models used are the ones for which the corresponding boolean is set to `True`. The evaluation is performed both individually and combined. This method can be used to evaluate different classes of models, such as `TriggerChannelModel` and `ActionChannelModel` on the common set of test examples, so as to determine their combined performance, such as the total error in predicting recipes' channels.
- def create_ensemble(args, model_class): Creates an ensemble of models defined by the `model_class` and passed command-line arguments `args`. The command-line arguments are used to create and restore desired models and subset of the test set. Args: args (Namespace): Namespace containing parsed arguments. model_class (:obj:`Model`): One of the child classes of the `Model` class. Returns: EnsembledModel: An ensembled model.
- def _log_args(self, args): Logs command-line arguments.
- def _log_configurations(config): Logs the configurations being used. Configurations refer to the values for various hyper-parameters of the model. Args: config: A configuration class, similar to `configs.PaperConfigurations`.

Implement the Python class `CombinedModel` described below.

Class description:
Model class that combines different types of models -- such as `TriggerChannelModel`, `ActionChannelModel`, and `EnsembledModel` -- and exposes methods to enable them to be evaluated or used on the same set of examples or inputs. Attributes: use_trigger_channel_model (bool): Set to `True` if the trained `TriggerChannelModel` is to be included in the cocktail of models. use_action_channel_model (bool): Set to `True` if the trained `ActionChannelModel` is to be included in the cocktail of models. use_action_fn_model (bool): Set to `True` if the trained `ActionFunctionModel` is to be included in the cocktail of models. use_trigger_fn_model (bool): Set to `True` if the trained `TriggerFunctionMo

Method signatures and docstrings:
- def __init__(self, use_trigger_channel_model=True, use_action_channel_model=True, use_trigger_fn_model=True, use_action_fn_model=True): Sets which types of models to include in the cocktail of models to be used together. Args: use_trigger_channel_model (bool): Add an ensemble of `TriggerChannelModel` models to the cocktail of models if `True`. Defaults to `True`. use_action_channel_model (bool): Add an ensemble of `ActionChannelModel` models to the cocktail of models if `True`. Defaults to `True`.: use_trigger_fn_model (bool): Add an ensemble of `TriggerFunctionModel` models to the cocktail of models if `True`. Defaults to `True`.: use_action_fn_model (bool): Add an ensemble of `ActionFunctionModel` models to the cocktail of models if `True`. Defaults to `True`.:
- def test_models(self): Evaluates the trained models on a common set of test examples. The models used are the ones for which the corresponding boolean is set to `True`. The evaluation is performed both individually and combined. This method can be used to evaluate different classes of models, such as `TriggerChannelModel` and `ActionChannelModel` on the common set of test examples, so as to determine their combined performance, such as the total error in predicting recipes' channels.
- def create_ensemble(args, model_class): Creates an ensemble of models defined by the `model_class` and passed command-line arguments `args`. The command-line arguments are used to create and restore desired models and subset of the test set. Args: args (Namespace): Namespace containing parsed arguments. model_class (:obj:`Model`): One of the child classes of the `Model` class. Returns: EnsembledModel: An ensembled model.
- def _log_args(self, args): Logs command-line arguments.
- def _log_configurations(config): Logs the configurations being used. Configurations refer to the values for various hyper-parameters of the model. Args: config: A configuration class, similar to `configs.PaperConfigurations`.

<|skeleton|>
class CombinedModel:
    """Model class that combines different types of models -- such as `TriggerChannelModel`, `ActionChannelModel`, and `EnsembledModel` -- and exposes methods to enable them to be evaluated or used on the same set of examples or inputs. Attributes: use_trigger_channel_model (bool): Set to `True` if the trained `TriggerChannelModel` is to be included in the cocktail of models. use_action_channel_model (bool): Set to `True` if the trained `ActionChannelModel` is to be included in the cocktail of models. use_action_fn_model (bool): Set to `True` if the trained `ActionFunctionModel` is to be included in the cocktail of models. use_trigger_fn_model (bool): Set to `True` if the trained `TriggerFunctionMo"""

    def __init__(self, use_trigger_channel_model=True, use_action_channel_model=True, use_trigger_fn_model=True, use_action_fn_model=True):
        """Sets which types of models to include in the cocktail of models to be used together. Args: use_trigger_channel_model (bool): Add an ensemble of `TriggerChannelModel` models to the cocktail of models if `True`. Defaults to `True`. use_action_channel_model (bool): Add an ensemble of `ActionChannelModel` models to the cocktail of models if `True`. Defaults to `True`.: use_trigger_fn_model (bool): Add an ensemble of `TriggerFunctionModel` models to the cocktail of models if `True`. Defaults to `True`.: use_action_fn_model (bool): Add an ensemble of `ActionFunctionModel` models to the cocktail of models if `True`. Defaults to `True`.:"""
        <|body_0|>

    def test_models(self):
        """Evaluates the trained models on a common set of test examples. The models used are the ones for which the corresponding boolean is set to `True`. The evaluation is performed both individually and combined. This method can be used to evaluate different classes of models, such as `TriggerChannelModel` and `ActionChannelModel` on the common set of test examples, so as to determine their combined performance, such as the total error in predicting recipes' channels."""
        <|body_1|>

    def create_ensemble(args, model_class):
        """Creates an ensemble of models defined by the `model_class` and passed command-line arguments `args`. The command-line arguments are used to create and restore desired models and subset of the test set. Args: args (Namespace): Namespace containing parsed arguments. model_class (:obj:`Model`): One of the child classes of the `Model` class. Returns: EnsembledModel: An ensembled model."""
        <|body_2|>

    def _log_args(self, args):
        """Logs command-line arguments."""
        <|body_3|>

    def _log_configurations(config):
        """Logs the configurations being used. Configurations refer to the values for various hyper-parameters of the model. Args: config: A configuration class, similar to `configs.PaperConfigurations`."""
        <|body_4|>

<|end_skeleton|>

<|body_start_0|>
        self.use_trigger_channel_model = use_trigger_channel_model
        self.use_action_channel_model = use_action_channel_model
        self.use_trigger_fn_model = use_trigger_fn_model
        self.use_action_fn_model = use_action_fn_model
<|end_body_0|>

<|body_start_1|>
        args, model_classes = ([], [])
        if self.use_trigger_channel_model:
            args.append(self.t_channel_args)
            model_classes.append(TriggerChannelModel)
        if self.use_action_channel_model:
            args.append(self.a_channel_args)
            model_classes.append(ActionChannelModel)
        if self.use_trigger_fn_model:
            args.append(self.t_fn_args)
            model_classes.append(TriggerFunctionModel)
        if self.use_action_fn_model:
            args.append(self.a_fn_args)
            model_classes.append(ActionFunctionModel)
        ensembles = []
        for arg, model_class in zip(args, model_classes):
            ensembles.append(self.create_ensemble(arg, model_class))
        n = len(ensembles[0].test_data()[0])
        mistakes = np.array([False] * n)
        for ensemble in ensembles:
            inputs, labels, seq_lens = ensemble.test_data()
            m = ensemble.prediction_mistakes(inputs, labels, seq_lens)
            logging.info('Individual error = %s', np.mean(m))
            mistakes = mistakes | m
        error = np.mean(mistakes)
        logging.info('Combined Error = %s', error)
<|end_body_1|>

<|body_start_2|>
        assert len(args.experiment_name) == len(args.saved_model_path)
        config = configs.PaperConfiguration
        CombinedModel._log_configurations(config)
        num_models = len(args.experiment_name)
        models = []
        for i in xrange(num_models):
            with tf.Graph().as_default() as graph:
                logging.info('Model number %s', i)
                expt_path = RNN_EXPT_DIRECTORY + args.experiment_name[i] + '/'
                model = model_class(config, expt_path, stem=True)
                model.load_labels_and_vocab()
                model.load_test_dataset(external_csv_file=args.external_test_csv, use_full_test_set=args.use_full_test_set, use_english=args.use_english, use_english_intelligible=args.use_english_intelligible, use_gold=args.use_gold, use_names_descriptions=args.use_names_descriptions)
                model.initialize_network(init_variables=False, graph=graph)
                model.restore(args.saved_model_path[i])
                models.append(model)
        ensemble = EnsembledModel()
        for model in models:
            ensemble.add_model(model)
        return ensemble
<|end_body_2|>

<|body_start_3|>
        logging.basicConfig(level=getattr(logging, args.log_level.upper()), format='%(levelname)s: %(asctime)s: %(message)s')
        logging.info('Log Level: %s', args.log_level)
        logging.info('Experiment Name: %s', args.experiment_name)
        logging.info('Model: %s', args.model[0])
        logging.info('Saved Model Path: %s', args.saved_model_path)
        logging.info('Use Full Test Set: %s', args.use_full_test_set)
        logging.info('Use English Subset: %s', args.use_english)
        logging.info('Use English and Intelligible Subset: %s', args.use_english_intelligible)
        logging.info('Use Gold Subset: %s', args.use_gold)
        logging.info('Use Names and Descriptions: %s', args.use_names_descriptions)
<|end_body_3|>

<|body_start_4|>
        logging.info('Using the following configurations:')
        logging.info('hidden_size (d) = %s', config.hidden_size)
        logging.info('vocab_size (N) = %s', config.vocab_size)
        logging.info('sent_size (j) = %s', config.sent_size)
<|end_body_4|>

Solution:
class CombinedModel:
    """Model class that combines different types of models -- such as `TriggerChannelModel`, `ActionChannelModel`, and `EnsembledModel` -- and exposes methods to enable them to be evaluated or used on the same set of examples or inputs. Attributes: use_trigger_channel_model (bool): Set to `True` if the trained `TriggerChannelModel` is to be included in the cocktail of models. use_action_channel_model (bool): Set to `True` if the trained `ActionChannelModel` is to be included in the cocktail of models. use_action_fn_model (bool): Set to `True` if the trained `ActionFunctionModel` is to be included in the cocktail of models. use_trigger_fn_model (bool): Set to `True` if the trained `TriggerFunctionMo"""

    def __init__(self, use_trigger_channel_model=True, use_action_channel_model=True, use_trigger_fn_model=True, use_action_fn_model=True):
        """Sets which types of models to include in the cocktail of models to be used together. Args: use_trigger_channel_model (bool): Add an ensemble of `TriggerChannelModel` models to the cocktail of models if `True`. Defaults to `True`. use_action_channel_model (bool): Add an ensemble of `ActionChannelModel` models to the cocktail of models if `True`. Defaults to `True`.: use_trigger_fn_model (bool): Add an ensemble of `TriggerFunctionModel` models to the cocktail of models if `True`. Defaults to `True`.: use_action_fn_model (bool): Add an ensemble of `ActionFunctionModel` models to the cocktail of models if `True`. Defaults to `True`.:"""
        self.use_trigger_channel_model = use_trigger_channel_model
        self.use_action_channel_model = use_action_channel_model
        self.use_trigger_fn_model = use_trigger_fn_model
        self.use_action_fn_model = use_action_fn_model

    def test_models(self):
        """Evaluates the trained models on a common set of test examples. The models used are the ones for which the corresponding boolean is set to `True`. The evaluation is performed both individually and combined. This method can be used to evaluate different classes of models, such as `TriggerChannelModel` and `ActionChannelModel` on the common set of test examples, so as to determine their combined performance, such as the total error in predicting recipes' channels."""
        args, model_classes = ([], [])
        if self.use_trigger_channel_model:
            args.append(self.t_channel_args)
            model_classes.append(TriggerChannelModel)
        if self.use_action_channel_model:
            args.append(self.a_channel_args)
            model_classes.append(ActionChannelModel)
        if self.use_trigger_fn_model:
            args.append(self.t_fn_args)
            model_classes.append(TriggerFunctionModel)
        if self.use_action_fn_model:
            args.append(self.a_fn_args)
            model_classes.append(ActionFunctionModel)
        ensembles = []
        for arg, model_class in zip(args, model_classes):
            ensembles.append(self.create_ensemble(arg, model_class))
        n = len(ensembles[0].test_data()[0])
        mistakes = np.array([False] * n)
        for ensemble in ensembles:
            inputs, labels, seq_lens = ensemble.test_data()
            m = ensemble.prediction_mistakes(inputs, labels, seq_lens)
            logging.info('Individual error = %s', np.mean(m))
            mistakes = mistakes | m
        error = np.mean(mistakes)
        logging.info('Combined Error = %s', error)

    def create_ensemble(args, model_class):
        """Creates an ensemble of models defined by the `model_class` and passed command-line arguments `args`. The command-line arguments are used to create and restore desired models and subset of the test set. Args: args (Namespace): Namespace containing parsed arguments. model_class (:obj:`Model`): One of the child classes of the `Model` class. Returns: EnsembledModel: An ensembled model."""
        assert len(args.experiment_name) == len(args.saved_model_path)
        config = configs.PaperConfiguration
        CombinedModel._log_configurations(config)
        num_models = len(args.experiment_name)
        models = []
        for i in xrange(num_models):
            with tf.Graph().as_default() as graph:
                logging.info('Model number %s', i)
                expt_path = RNN_EXPT_DIRECTORY + args.experiment_name[i] + '/'
                model = model_class(config, expt_path, stem=True)
                model.load_labels_and_vocab()
                model.load_test_dataset(external_csv_file=args.external_test_csv, use_full_test_set=args.use_full_test_set, use_english=args.use_english, use_english_intelligible=args.use_english_intelligible, use_gold=args.use_gold, use_names_descriptions=args.use_names_descriptions)
                model.initialize_network(init_variables=False, graph=graph)
                model.restore(args.saved_model_path[i])
                models.append(model)
        ensemble = EnsembledModel()
        for model in models:
            ensemble.add_model(model)
        return ensemble

    def _log_args(self, args):
        """Logs command-line arguments."""
        logging.basicConfig(level=getattr(logging, args.log_level.upper()), format='%(levelname)s: %(asctime)s: %(message)s')
        logging.info('Log Level: %s', args.log_level)
        logging.info('Experiment Name: %s', args.experiment_name)
        logging.info('Model: %s', args.model[0])
        logging.info('Saved Model Path: %s', args.saved_model_path)
        logging.info('Use Full Test Set: %s', args.use_full_test_set)
        logging.info('Use English Subset: %s', args.use_english)
        logging.info('Use English and Intelligible Subset: %s', args.use_english_intelligible)
        logging.info('Use Gold Subset: %s', args.use_gold)
        logging.info('Use Names and Descriptions: %s', args.use_names_descriptions)

    def _log_configurations(config):
        """Logs the configurations being used. Configurations refer to the values for various hyper-parameters of the model. Args: config: A configuration class, similar to `configs.PaperConfigurations`."""
        logging.info('Using the following configurations:')
        logging.info('hidden_size (d) = %s', config.hidden_size)
        logging.info('vocab_size (N) = %s', config.vocab_size)
        logging.info('sent_size (j) = %s', config.sent_size)


forward

Prompt:
Implement the Python class `FieldBatchNormalization` described below.

Class description:
Field convolution layer.

Method signatures and docstrings:
- def __init__(self, in_channel: int): The initialization function. Args: in_channel: The number of input channels.
- def forward(self, in_feature: torch.Tensor): The forward function. Args: in_feature: The input feature with the concatenation of the following two tensors. points: The input point clouds. (B, N, 3) feature: The signed distance fields. (B, N, Fin) Returns: out_feature: The output feature with the concatenation of the following two tensors. points: The input point clouds. (B, Nc, 3) feature: The signed distance fields. (B, Nc, Fout)

Implement the Python class `FieldBatchNormalization` described below.

Class description:
Field convolution layer.

Method signatures and docstrings:
- def __init__(self, in_channel: int): The initialization function. Args: in_channel: The number of input channels.
- def forward(self, in_feature: torch.Tensor): The forward function. Args: in_feature: The input feature with the concatenation of the following two tensors. points: The input point clouds. (B, N, 3) feature: The signed distance fields. (B, N, Fin) Returns: out_feature: The output feature with the concatenation of the following two tensors. points: The input point clouds. (B, Nc, 3) feature: The signed distance fields. (B, Nc, Fout)

<|skeleton|>
class FieldBatchNormalization:
    """Field convolution layer."""

    def __init__(self, in_channel: int):
        """The initialization function. Args: in_channel: The number of input channels."""
        <|body_0|>

    def forward(self, in_feature: torch.Tensor):
        """The forward function. Args: in_feature: The input feature with the concatenation of the following two tensors. points: The input point clouds. (B, N, 3) feature: The signed distance fields. (B, N, Fin) Returns: out_feature: The output feature with the concatenation of the following two tensors. points: The input point clouds. (B, Nc, 3) feature: The signed distance fields. (B, Nc, Fout)"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super(FieldBatchNormalization, self).__init__()
        self.bn = nn.BatchNorm1d(in_channel)
        self.in_channel = in_channel
<|end_body_0|>

<|body_start_1|>
        points = in_feature[:, :, :3]
        feature = in_feature[:, :, 3:]
        feature = feature.permute(0, 2, 1)
        assert len(points.shape) == 3, 'The input point cloud should be batched!'
        assert len(feature.shape) == 3, 'The input signed distance field should be batched!'
        assert points.shape[2] == 3, 'The input point cloud should be in 3D space!'
        bn_feature = self.bn(feature)
        bn_feature = bn_feature.permute(0, 2, 1)
        out_feature = torch.cat([points, bn_feature], -1)
        return out_feature
<|end_body_1|>

Solution:
class FieldBatchNormalization:
    """Field convolution layer."""

    def __init__(self, in_channel: int):
        """The initialization function. Args: in_channel: The number of input channels."""
        super(FieldBatchNormalization, self).__init__()
        self.bn = nn.BatchNorm1d(in_channel)
        self.in_channel = in_channel

    def forward(self, in_feature: torch.Tensor):
        """The forward function. Args: in_feature: The input feature with the concatenation of the following two tensors. points: The input point clouds. (B, N, 3) feature: The signed distance fields. (B, N, Fin) Returns: out_feature: The output feature with the concatenation of the following two tensors. points: The input point clouds. (B, Nc, 3) feature: The signed distance fields. (B, Nc, Fout)"""
        points = in_feature[:, :, :3]
        feature = in_feature[:, :, 3:]
        feature = feature.permute(0, 2, 1)
        assert len(points.shape) == 3, 'The input point cloud should be batched!'
        assert len(feature.shape) == 3, 'The input signed distance field should be batched!'
        assert points.shape[2] == 3, 'The input point cloud should be in 3D space!'
        bn_feature = self.bn(feature)
        bn_feature = bn_feature.permute(0, 2, 1)
        out_feature = torch.cat([points, bn_feature], -1)
        return out_feature


Prompt:
Implement the Python class `MatcherTestCase` described below.

Class description:
Tests for the CLB matchers.

Method signatures and docstrings:
- def test_contains_all_ips_success(self): :class:`ContainsAllIPs` succeeds when the nodes contain all the IPs given.
- def test_contains_all_ips_failure(self): :class:`ContainsAllIPs` fail when the nodes contain only some or none of the all the IPs given.
- def test_excludes_all_ips_success(self): :class:`ExcludesAllIPs` succeeds when the nodes do not contain any of the IPs given.
- def test_excludes_all_ips_failure(self): :class:`ExcludesAllIPs` fails when the nodes contain any or all of the IPs given.
- def test_has_length(self): :class:`HasLength` only succeeds when the number of nodes matches the length given.

Implement the Python class `MatcherTestCase` described below.

Class description:
Tests for the CLB matchers.

Method signatures and docstrings:
- def test_contains_all_ips_success(self): :class:`ContainsAllIPs` succeeds when the nodes contain all the IPs given.
- def test_contains_all_ips_failure(self): :class:`ContainsAllIPs` fail when the nodes contain only some or none of the all the IPs given.
- def test_excludes_all_ips_success(self): :class:`ExcludesAllIPs` succeeds when the nodes do not contain any of the IPs given.
- def test_excludes_all_ips_failure(self): :class:`ExcludesAllIPs` fails when the nodes contain any or all of the IPs given.
- def test_has_length(self): :class:`HasLength` only succeeds when the number of nodes matches the length given.

<|skeleton|>
class MatcherTestCase:
    """Tests for the CLB matchers."""

    def test_contains_all_ips_success(self):
        """:class:`ContainsAllIPs` succeeds when the nodes contain all the IPs given."""
        <|body_0|>

    def test_contains_all_ips_failure(self):
        """:class:`ContainsAllIPs` fail when the nodes contain only some or none of the all the IPs given."""
        <|body_1|>

    def test_excludes_all_ips_success(self):
        """:class:`ExcludesAllIPs` succeeds when the nodes do not contain any of the IPs given."""
        <|body_2|>

    def test_excludes_all_ips_failure(self):
        """:class:`ExcludesAllIPs` fails when the nodes contain any or all of the IPs given."""
        <|body_3|>

    def test_has_length(self):
        """:class:`HasLength` only succeeds when the number of nodes matches the length given."""
        <|body_4|>

<|end_skeleton|>

<|body_start_0|>
        matcher = ContainsAllIPs(['10.0.0.1', '10.0.0.2', '10.0.0.2'])
        mismatch = matcher.match([{'id': i, 'address': '10.0.0.{0}'.format(i)} for i in (1, 2)])
        self.assertEqual(None, mismatch)
<|end_body_0|>

<|body_start_1|>
        matcher = ContainsAllIPs(['10.0.0.1', '10.0.0.2', '10.0.0.2'])
        self.assertNotEqual(None, matcher.match([{'id': i, 'address': '10.0.0.{0}'.format(i)} for i in (1, 3)]), 'Partial match succeeds when all should be required.')
        self.assertNotEqual(None, matcher.match([]), 'No matches succed.')
<|end_body_1|>

<|body_start_2|>
        matcher = ExcludesAllIPs(['10.0.0.1', '10.0.0.1'])
        mismatch = matcher.match([{'id': i, 'address': '10.0.0.{0}'.format(i)} for i in (2, 3)])
        self.assertEqual(None, mismatch)
<|end_body_2|>

<|body_start_3|>
        matcher = ExcludesAllIPs(['10.0.0.1', '10.0.0.2'])
        self.assertNotEqual(None, matcher.match([{'id': i, 'address': '10.0.0.{0}'.format(i)} for i in (1, 2)]), 'Complete match succeeds when none should be present.')
        self.assertNotEqual(None, matcher.match([{'id': 1, 'address': '10.0.0.1'}]), 'Partial match succeeds when none should be present.')
<|end_body_3|>

<|body_start_4|>
        matcher = HasLength(2)
        self.assertNotEqual(None, matcher.match([{'id': 1, 'address': '10.0.0.1'}]))
        self.assertNotEqual(None, matcher.match([]))
        self.assertEqual(None, matcher.match([{'id': i, 'address': '10.0.0.{0}'.format(i)} for i in (1, 2)]))
<|end_body_4|>

Solution:
class MatcherTestCase:
    """Tests for the CLB matchers."""

    def test_contains_all_ips_success(self):
        """:class:`ContainsAllIPs` succeeds when the nodes contain all the IPs given."""
        matcher = ContainsAllIPs(['10.0.0.1', '10.0.0.2', '10.0.0.2'])
        mismatch = matcher.match([{'id': i, 'address': '10.0.0.{0}'.format(i)} for i in (1, 2)])
        self.assertEqual(None, mismatch)

    def test_contains_all_ips_failure(self):
        """:class:`ContainsAllIPs` fail when the nodes contain only some or none of the all the IPs given."""
        matcher = ContainsAllIPs(['10.0.0.1', '10.0.0.2', '10.0.0.2'])
        self.assertNotEqual(None, matcher.match([{'id': i, 'address': '10.0.0.{0}'.format(i)} for i in (1, 3)]), 'Partial match succeeds when all should be required.')
        self.assertNotEqual(None, matcher.match([]), 'No matches succed.')

    def test_excludes_all_ips_success(self):
        """:class:`ExcludesAllIPs` succeeds when the nodes do not contain any of the IPs given."""
        matcher = ExcludesAllIPs(['10.0.0.1', '10.0.0.1'])
        mismatch = matcher.match([{'id': i, 'address': '10.0.0.{0}'.format(i)} for i in (2, 3)])
        self.assertEqual(None, mismatch)

    def test_excludes_all_ips_failure(self):
        """:class:`ExcludesAllIPs` fails when the nodes contain any or all of the IPs given."""
        matcher = ExcludesAllIPs(['10.0.0.1', '10.0.0.2'])
        self.assertNotEqual(None, matcher.match([{'id': i, 'address': '10.0.0.{0}'.format(i)} for i in (1, 2)]), 'Complete match succeeds when none should be present.')
        self.assertNotEqual(None, matcher.match([{'id': 1, 'address': '10.0.0.1'}]), 'Partial match succeeds when none should be present.')

    def test_has_length(self):
        """:class:`HasLength` only succeeds when the number of nodes matches the length given."""
        matcher = HasLength(2)
        self.assertNotEqual(None, matcher.match([{'id': 1, 'address': '10.0.0.1'}]))
        self.assertNotEqual(None, matcher.match([]))
        self.assertEqual(None, matcher.match([{'id': i, 'address': '10.0.0.{0}'.format(i)} for i in (1, 2)]))


Prompt:
Implement the Python class `AzureTableReader` described below.

Class description:
This reader reads data from given table Attributes ---------- logger : AirbyteLogger Airbyte's Logger instance account_name : str The name of your storage account. access_key : str The access key to your storage account. Read more about access keys here - https://docs.microsoft.com/en-us/azure/storage/common/storage-account-keys-manage?tabs=azure-portal#view-account-access-keys endpoint_suffix : str The Table service account URL suffix. Read more about suffixes here - https://docs.microsoft.com/en-us/azure/storage/common/storage-configure-connection-string#create-a-connection-string-with-an-endpoint-suffix connection_string: str storage account connection string created using above params. R

Method signatures and docstrings:
- def __init__(self, logger: AirbyteLogger, config: dict): Parameters ---------- config : dict Airbyte's configuration obect
- def get_table_service_client(self) -> TableServiceClient: Returns azure table service client from connection string. Table service client facilitate interaction with tables. Please read more here - https://docs.microsoft.com/en-us/rest/api/storageservices/operations-on-tables
- def get_table_client(self, table_name: str) -> TableClient: Returns azure table client from connection string. Table client facilitate interaction with table entities/rows. Please read more here - https://docs.microsoft.com/en-us/rest/api/storageservices/operations-on-entities Parameters ---------- table_name : str table name for which you would like create table client for.
- def get_tables(self) -> ItemPaged: Fetches all tables from storage account and returns them in Airbyte stream.
- def read_table(self, table_client: TableClient, filter_query: str=None) -> Iterable: Reads data from an Azure table. Parameters ---------- table_client : TableClient table client object to be able to access querying methods. filter_query : str either None or a query to pull data from table storage (based on the PartitionKey)

Implement the Python class `AzureTableReader` described below.

Class description:
This reader reads data from given table Attributes ---------- logger : AirbyteLogger Airbyte's Logger instance account_name : str The name of your storage account. access_key : str The access key to your storage account. Read more about access keys here - https://docs.microsoft.com/en-us/azure/storage/common/storage-account-keys-manage?tabs=azure-portal#view-account-access-keys endpoint_suffix : str The Table service account URL suffix. Read more about suffixes here - https://docs.microsoft.com/en-us/azure/storage/common/storage-configure-connection-string#create-a-connection-string-with-an-endpoint-suffix connection_string: str storage account connection string created using above params. R

Method signatures and docstrings:
- def __init__(self, logger: AirbyteLogger, config: dict): Parameters ---------- config : dict Airbyte's configuration obect
- def get_table_service_client(self) -> TableServiceClient: Returns azure table service client from connection string. Table service client facilitate interaction with tables. Please read more here - https://docs.microsoft.com/en-us/rest/api/storageservices/operations-on-tables
- def get_table_client(self, table_name: str) -> TableClient: Returns azure table client from connection string. Table client facilitate interaction with table entities/rows. Please read more here - https://docs.microsoft.com/en-us/rest/api/storageservices/operations-on-entities Parameters ---------- table_name : str table name for which you would like create table client for.
- def get_tables(self) -> ItemPaged: Fetches all tables from storage account and returns them in Airbyte stream.
- def read_table(self, table_client: TableClient, filter_query: str=None) -> Iterable: Reads data from an Azure table. Parameters ---------- table_client : TableClient table client object to be able to access querying methods. filter_query : str either None or a query to pull data from table storage (based on the PartitionKey)

<|skeleton|>
class AzureTableReader:
    """This reader reads data from given table Attributes ---------- logger : AirbyteLogger Airbyte's Logger instance account_name : str The name of your storage account. access_key : str The access key to your storage account. Read more about access keys here - https://docs.microsoft.com/en-us/azure/storage/common/storage-account-keys-manage?tabs=azure-portal#view-account-access-keys endpoint_suffix : str The Table service account URL suffix. Read more about suffixes here - https://docs.microsoft.com/en-us/azure/storage/common/storage-configure-connection-string#create-a-connection-string-with-an-endpoint-suffix connection_string: str storage account connection string created using above params. R"""

    def __init__(self, logger: AirbyteLogger, config: dict):
        """Parameters ---------- config : dict Airbyte's configuration obect"""
        <|body_0|>

    def get_table_service_client(self) -> TableServiceClient:
        """Returns azure table service client from connection string. Table service client facilitate interaction with tables. Please read more here - https://docs.microsoft.com/en-us/rest/api/storageservices/operations-on-tables"""
        <|body_1|>

    def get_table_client(self, table_name: str) -> TableClient:
        """Returns azure table client from connection string. Table client facilitate interaction with table entities/rows. Please read more here - https://docs.microsoft.com/en-us/rest/api/storageservices/operations-on-entities Parameters ---------- table_name : str table name for which you would like create table client for."""
        <|body_2|>

    def get_tables(self) -> ItemPaged:
        """Fetches all tables from storage account and returns them in Airbyte stream."""
        <|body_3|>

    def read_table(self, table_client: TableClient, filter_query: str=None) -> Iterable:
        """Reads data from an Azure table. Parameters ---------- table_client : TableClient table client object to be able to access querying methods. filter_query : str either None or a query to pull data from table storage (based on the PartitionKey)"""
        <|body_4|>

<|end_skeleton|>

<|body_start_0|>
        self.logger = logger
        self.account_name = config[constants.azure_storage_account_name_key_name]
        self.access_key = config[constants.azure_storage_access_key_key_name]
        self.endpoint_suffix = config[constants.azure_storage_endpoint_suffix_key_name]
        self.connection_string = 'DefaultEndpointsProtocol=https;AccountName={};AccountKey={};EndpointSuffix={}'.format(self.account_name, self.access_key, self.endpoint_suffix)
<|end_body_0|>

<|body_start_1|>
        try:
            return TableServiceClient.from_connection_string(conn_str=self.connection_string)
        except Exception as e:
            raise Exception(f'An exception occurred: {str(e)}')
<|end_body_1|>

<|body_start_2|>
        try:
            if not table_name:
                raise Exception('An exception occurred: table name is not valid.')
            return TableClient.from_connection_string(self.connection_string, table_name=table_name)
        except Exception as e:
            raise Exception(f'An exception occurred: {str(e)}')
<|end_body_2|>

<|body_start_3|>
        try:
            table_service_client = self.get_table_service_client()
            tables_iterator = table_service_client.list_tables(results_per_page=constants.results_per_page)
            return tables_iterator
        except Exception as e:
            raise Exception(f'An exception occurred: {str(e)}')
<|end_body_3|>

<|body_start_4|>
        if filter_query is None:
            return table_client.list_entities()
        else:
            return table_client.query_entities(query_filter=filter_query, results_per_page=constants.results_per_page)
<|end_body_4|>

Solution:
class AzureTableReader:
    """This reader reads data from given table Attributes ---------- logger : AirbyteLogger Airbyte's Logger instance account_name : str The name of your storage account. access_key : str The access key to your storage account. Read more about access keys here - https://docs.microsoft.com/en-us/azure/storage/common/storage-account-keys-manage?tabs=azure-portal#view-account-access-keys endpoint_suffix : str The Table service account URL suffix. Read more about suffixes here - https://docs.microsoft.com/en-us/azure/storage/common/storage-configure-connection-string#create-a-connection-string-with-an-endpoint-suffix connection_string: str storage account connection string created using above params. R"""

    def __init__(self, logger: AirbyteLogger, config: dict):
        """Parameters ---------- config : dict Airbyte's configuration obect"""
        self.logger = logger
        self.account_name = config[constants.azure_storage_account_name_key_name]
        self.access_key = config[constants.azure_storage_access_key_key_name]
        self.endpoint_suffix = config[constants.azure_storage_endpoint_suffix_key_name]
        self.connection_string = 'DefaultEndpointsProtocol=https;AccountName={};AccountKey={};EndpointSuffix={}'.format(self.account_name, self.access_key, self.endpoint_suffix)

    def get_table_service_client(self) -> TableServiceClient:
        """Returns azure table service client from connection string. Table service client facilitate interaction with tables. Please read more here - https://docs.microsoft.com/en-us/rest/api/storageservices/operations-on-tables"""
        try:
            return TableServiceClient.from_connection_string(conn_str=self.connection_string)
        except Exception as e:
            raise Exception(f'An exception occurred: {str(e)}')

    def get_table_client(self, table_name: str) -> TableClient:
        """Returns azure table client from connection string. Table client facilitate interaction with table entities/rows. Please read more here - https://docs.microsoft.com/en-us/rest/api/storageservices/operations-on-entities Parameters ---------- table_name : str table name for which you would like create table client for."""
        try:
            if not table_name:
                raise Exception('An exception occurred: table name is not valid.')
            return TableClient.from_connection_string(self.connection_string, table_name=table_name)
        except Exception as e:
            raise Exception(f'An exception occurred: {str(e)}')

    def get_tables(self) -> ItemPaged:
        """Fetches all tables from storage account and returns them in Airbyte stream."""
        try:
            table_service_client = self.get_table_service_client()
            tables_iterator = table_service_client.list_tables(results_per_page=constants.results_per_page)
            return tables_iterator
        except Exception as e:
            raise Exception(f'An exception occurred: {str(e)}')

    def read_table(self, table_client: TableClient, filter_query: str=None) -> Iterable:
        """Reads data from an Azure table. Parameters ---------- table_client : TableClient table client object to be able to access querying methods. filter_query : str either None or a query to pull data from table storage (based on the PartitionKey)"""
        if filter_query is None:
            return table_client.list_entities()
        else:
            return table_client.query_entities(query_filter=filter_query, results_per_page=constants.results_per_page)


Prompt:
Implement the Python class `SourcesForSid` described below.

Class description:
Implementation of the 'SourcesForSid' model. Protection Sources and Views With Access Permissions. Specifies the Protection Sources objects and Views that the specified principal has permissions to access. The principal is specified using a security identifier (SID). Attributes: protection_sources (list of ProtectionSource): Array of Protection Sources. Specifies the Protection Source objects that the specified principal has permissions to access. sid (string): Specifies the security identifier (SID) of the principal. views (list of View): Array of View Names. Specifies the names of the Views that the specified principal has permissions to access.

Method signatures and docstrings:
- def __init__(self, protection_sources=None, sid=None, views=None): Constructor for the SourcesForSid class
- def from_dictionary(cls, dictionary): Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the object as obtained from the deserialization of the server's response. The keys MUST match property names in the API description. Returns: object: An instance of this structure class.

Implement the Python class `SourcesForSid` described below.

Class description:
Implementation of the 'SourcesForSid' model. Protection Sources and Views With Access Permissions. Specifies the Protection Sources objects and Views that the specified principal has permissions to access. The principal is specified using a security identifier (SID). Attributes: protection_sources (list of ProtectionSource): Array of Protection Sources. Specifies the Protection Source objects that the specified principal has permissions to access. sid (string): Specifies the security identifier (SID) of the principal. views (list of View): Array of View Names. Specifies the names of the Views that the specified principal has permissions to access.

Method signatures and docstrings:
- def __init__(self, protection_sources=None, sid=None, views=None): Constructor for the SourcesForSid class
- def from_dictionary(cls, dictionary): Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the object as obtained from the deserialization of the server's response. The keys MUST match property names in the API description. Returns: object: An instance of this structure class.

<|skeleton|>
class SourcesForSid:
    """Implementation of the 'SourcesForSid' model. Protection Sources and Views With Access Permissions. Specifies the Protection Sources objects and Views that the specified principal has permissions to access. The principal is specified using a security identifier (SID). Attributes: protection_sources (list of ProtectionSource): Array of Protection Sources. Specifies the Protection Source objects that the specified principal has permissions to access. sid (string): Specifies the security identifier (SID) of the principal. views (list of View): Array of View Names. Specifies the names of the Views that the specified principal has permissions to access."""

    def __init__(self, protection_sources=None, sid=None, views=None):
        """Constructor for the SourcesForSid class"""
        <|body_0|>

    def from_dictionary(cls, dictionary):
        """Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the object as obtained from the deserialization of the server's response. The keys MUST match property names in the API description. Returns: object: An instance of this structure class."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.protection_sources = protection_sources
        self.sid = sid
        self.views = views
<|end_body_0|>

<|body_start_1|>
        if dictionary is None:
            return None
        protection_sources = None
        if dictionary.get('protectionSources') != None:
            protection_sources = list()
            for structure in dictionary.get('protectionSources'):
                protection_sources.append(cohesity_management_sdk.models.protection_source.ProtectionSource.from_dictionary(structure))
        sid = dictionary.get('sid')
        views = None
        if dictionary.get('views') != None:
            views = list()
            for structure in dictionary.get('views'):
                views.append(cohesity_management_sdk.models.view.View.from_dictionary(structure))
        return cls(protection_sources, sid, views)
<|end_body_1|>

Solution:
class SourcesForSid:
    """Implementation of the 'SourcesForSid' model. Protection Sources and Views With Access Permissions. Specifies the Protection Sources objects and Views that the specified principal has permissions to access. The principal is specified using a security identifier (SID). Attributes: protection_sources (list of ProtectionSource): Array of Protection Sources. Specifies the Protection Source objects that the specified principal has permissions to access. sid (string): Specifies the security identifier (SID) of the principal. views (list of View): Array of View Names. Specifies the names of the Views that the specified principal has permissions to access."""

    def __init__(self, protection_sources=None, sid=None, views=None):
        """Constructor for the SourcesForSid class"""
        self.protection_sources = protection_sources
        self.sid = sid
        self.views = views

    def from_dictionary(cls, dictionary):
        """Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the object as obtained from the deserialization of the server's response. The keys MUST match property names in the API description. Returns: object: An instance of this structure class."""
        if dictionary is None:
            return None
        protection_sources = None
        if dictionary.get('protectionSources') != None:
            protection_sources = list()
            for structure in dictionary.get('protectionSources'):
                protection_sources.append(cohesity_management_sdk.models.protection_source.ProtectionSource.from_dictionary(structure))
        sid = dictionary.get('sid')
        views = None
        if dictionary.get('views') != None:
            views = list()
            for structure in dictionary.get('views'):
                views.append(cohesity_management_sdk.models.view.View.from_dictionary(structure))
        return cls(protection_sources, sid, views)


Prompt:
Implement the Python class `UIObject` described below.

Class description:
UIObject definition

Method signatures and docstrings:
- def __init__(self, controller, element_tag=None, xpath=None): Constructor. :param controller: Main Controller :param element_tag: The path of the ui object
- def _set_element(self): Creates the link between the class and the UI element. Virtualizes the UI element.
- def is_visible(self): Checks if the element is visible or not. :return: True if the element of visible, False otherwise.
- def is_enabled(self): Checks if the element is enabled or not. :return: True if the element is enabled, False otherwise.
- def get_attribute(self, name): Returns an attribute related to the element. :param name: the name of the attribute. :return: returns the value of the attribute.
- def _is_interactable(self): Checks if the element is interactable of not. * if it is enabled and visible.

Implement the Python class `UIObject` described below.

Class description:
UIObject definition

Method signatures and docstrings:
- def __init__(self, controller, element_tag=None, xpath=None): Constructor. :param controller: Main Controller :param element_tag: The path of the ui object
- def _set_element(self): Creates the link between the class and the UI element. Virtualizes the UI element.
- def is_visible(self): Checks if the element is visible or not. :return: True if the element of visible, False otherwise.
- def is_enabled(self): Checks if the element is enabled or not. :return: True if the element is enabled, False otherwise.
- def get_attribute(self, name): Returns an attribute related to the element. :param name: the name of the attribute. :return: returns the value of the attribute.
- def _is_interactable(self): Checks if the element is interactable of not. * if it is enabled and visible.

<|skeleton|>
class UIObject:
    """UIObject definition"""

    def __init__(self, controller, element_tag=None, xpath=None):
        """Constructor. :param controller: Main Controller :param element_tag: The path of the ui object"""
        <|body_0|>

    def _set_element(self):
        """Creates the link between the class and the UI element. Virtualizes the UI element."""
        <|body_1|>

    def is_visible(self):
        """Checks if the element is visible or not. :return: True if the element of visible, False otherwise."""
        <|body_2|>

    def is_enabled(self):
        """Checks if the element is enabled or not. :return: True if the element is enabled, False otherwise."""
        <|body_3|>

    def get_attribute(self, name):
        """Returns an attribute related to the element. :param name: the name of the attribute. :return: returns the value of the attribute."""
        <|body_4|>

    def _is_interactable(self):
        """Checks if the element is interactable of not. * if it is enabled and visible."""
        <|body_5|>

<|end_skeleton|>

<|body_start_0|>
        self._element_tag = element_tag
        self._xpath = xpath
        self.__main_controller = controller
        self._element = None
<|end_body_0|>

<|body_start_1|>
        try:
            self._element = self.__main_controller.appium.get_element(self._element_tag)
        except NoSuchUIElement:
            index_ = 0
            success = False
            while index_ < Settings.Errors.RETRY_NUMBER and (not success):
                time.sleep(Settings.Waits.SMALL_SLEEP_TIME)
                try:
                    self._element = self.__main_controller.appium.get_element(self._element_tag)
                    success = True
                    break
                except NoSuchUIElement:
                    pass
                index_ += 1
            if not success:
                raise NoSuchUIElement()
<|end_body_1|>

<|body_start_2|>
        if self._element is None:
            try:
                self._set_element()
            except NoSuchUIElement:
                return False
        return self._element.is_displayed()
<|end_body_2|>

<|body_start_3|>
        if self._element is None:
            try:
                self._set_element()
            except NoSuchUIElement:
                return False
        return self._element.is_enabled()
<|end_body_3|>

<|body_start_4|>
        if self._element is None:
            self._set_element()
        return self._element.get_attibute(name)
<|end_body_4|>

<|body_start_5|>
        if self._element is None:
            self._set_element()
        if self._element.is_displayed() and self._element.is_enabled():
            return True
        return False
<|end_body_5|>

Solution:
class UIObject:
    """UIObject definition"""

    def __init__(self, controller, element_tag=None, xpath=None):
        """Constructor. :param controller: Main Controller :param element_tag: The path of the ui object"""
        self._element_tag = element_tag
        self._xpath = xpath
        self.__main_controller = controller
        self._element = None

    def _set_element(self):
        """Creates the link between the class and the UI element. Virtualizes the UI element."""
        try:
            self._element = self.__main_controller.appium.get_element(self._element_tag)
        except NoSuchUIElement:
            index_ = 0
            success = False
            while index_ < Settings.Errors.RETRY_NUMBER and (not success):
                time.sleep(Settings.Waits.SMALL_SLEEP_TIME)
                try:
                    self._element = self.__main_controller.appium.get_element(self._element_tag)
                    success = True
                    break
                except NoSuchUIElement:
                    pass
                index_ += 1
            if not success:
                raise NoSuchUIElement()

    def is_visible(self):
        """Checks if the element is visible or not. :return: True if the element of visible, False otherwise."""
        if self._element is None:
            try:
                self._set_element()
            except NoSuchUIElement:
                return False
        return self._element.is_displayed()

    def is_enabled(self):
        """Checks if the element is enabled or not. :return: True if the element is enabled, False otherwise."""
        if self._element is None:
            try:
                self._set_element()
            except NoSuchUIElement:
                return False
        return self._element.is_enabled()

    def get_attribute(self, name):
        """Returns an attribute related to the element. :param name: the name of the attribute. :return: returns the value of the attribute."""
        if self._element is None:
            self._set_element()
        return self._element.get_attibute(name)

    def _is_interactable(self):
        """Checks if the element is interactable of not. * if it is enabled and visible."""
        if self._element is None:
            self._set_element()
        if self._element.is_displayed() and self._element.is_enabled():
            return True
        return False


execute

Prompt:
Implement the Python class `Validator` described below.

Class description:
Provides an API to Check if each IDL file follows rules defined in Web IDL by validating an instance of web_idl.Database.

Method signatures and docstrings:
- def __init__(self, web_idl_database): Instantiates with web_idl.Database.
- def execute(self, rule_store, report_error): Validates `_web_idl_database` follows the rules stored in `rule_store`. Args: rule_store: A RuleStore which holds rules. report_error: A function to handle a detected error. It takes a Rule object, a target object, a debug_info, and an error_message. Returns: The number of validation errors.

Implement the Python class `Validator` described below.

Class description:
Provides an API to Check if each IDL file follows rules defined in Web IDL by validating an instance of web_idl.Database.

Method signatures and docstrings:
- def __init__(self, web_idl_database): Instantiates with web_idl.Database.
- def execute(self, rule_store, report_error): Validates `_web_idl_database` follows the rules stored in `rule_store`. Args: rule_store: A RuleStore which holds rules. report_error: A function to handle a detected error. It takes a Rule object, a target object, a debug_info, and an error_message. Returns: The number of validation errors.

<|skeleton|>
class Validator:
    """Provides an API to Check if each IDL file follows rules defined in Web IDL by validating an instance of web_idl.Database."""

    def __init__(self, web_idl_database):
        """Instantiates with web_idl.Database."""
        <|body_0|>

    def execute(self, rule_store, report_error):
        """Validates `_web_idl_database` follows the rules stored in `rule_store`. Args: rule_store: A RuleStore which holds rules. report_error: A function to handle a detected error. It takes a Rule object, a target object, a debug_info, and an error_message. Returns: The number of validation errors."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        assert isinstance(web_idl_database, web_idl.Database)
        self._web_idl_database = web_idl_database
        self._target_store = TargetStore(web_idl_database)
<|end_body_0|>

<|body_start_1|>
        assert isinstance(rule_store, RuleStore)
        rule = None
        target_type = None
        target_object = None

        def assert_(condition, text, *args, **kwargs):
            if not condition:
                error_message = text.format(*args, **kwargs)
                report_error(rule=rule, target=target_object, target_type=target_type, error_message=error_message)
        for target_type in rule_store.all_target_types:
            rules = rule_store.get_rules(target_type)
            target_objects = self._target_store.get(target_type)
            for target_object in target_objects:
                for rule in rules:
                    assert isinstance(rule, RuleBase)
                    rule.validate(assert_, target_object)
<|end_body_1|>

Solution:
class Validator:
    """Provides an API to Check if each IDL file follows rules defined in Web IDL by validating an instance of web_idl.Database."""

    def __init__(self, web_idl_database):
        """Instantiates with web_idl.Database."""
        assert isinstance(web_idl_database, web_idl.Database)
        self._web_idl_database = web_idl_database
        self._target_store = TargetStore(web_idl_database)

    def execute(self, rule_store, report_error):
        """Validates `_web_idl_database` follows the rules stored in `rule_store`. Args: rule_store: A RuleStore which holds rules. report_error: A function to handle a detected error. It takes a Rule object, a target object, a debug_info, and an error_message. Returns: The number of validation errors."""
        assert isinstance(rule_store, RuleStore)
        rule = None
        target_type = None
        target_object = None

        def assert_(condition, text, *args, **kwargs):
            if not condition:
                error_message = text.format(*args, **kwargs)
                report_error(rule=rule, target=target_object, target_type=target_type, error_message=error_message)
        for target_type in rule_store.all_target_types:
            rules = rule_store.get_rules(target_type)
            target_objects = self._target_store.get(target_type)
            for target_object in target_objects:
                for rule in rules:
                    assert isinstance(rule, RuleBase)
                    rule.validate(assert_, target_object)