Applies 2D adaptive avg pooling on input tensor. The h and w dimensions of the output tensor are determined by the parameter output_size.

For avg adaptive pool2d: .. math:

Unexpected indentation.

```hstart &= floor(i * H_{in} / H_{out})
hend &= ceil((i + 1) * H_{in} / H_{out})
wstart &= floor(j * W_{in} / W_{out})
wend &= ceil((j + 1) * W_{in} / W_{out})
Output(i ,j) &=
```

Block quote ends without a blank line; unexpected unindent.

rac{sum Input[hstart:hend, wstart:wend]}{(hend - hstart) * (wend - wstart)}

Args:
x (Tensor): The input tensor of adaptive avg pool2d operator, which is a 4-D tensor.

The data type can be float32 or float64.

output_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,

it must contain two element, (H, W). H and W can be either a int, or None which means the size will be the same as that of the input.

data_format (str): The data format of the input and output data. An optional string

from: “NCHW”, “NHWC”. The default is “NCHW”. When it is “NCHW”, the data is stored in the order of: [batch_size, input_channels, input_height, input_width].

name(str, optional): For detailed information, please refer

to Name. Usually name is no need to set and None by default.

Returns:

Tensor: The output tensor of avg adaptive pool2d result. The data type is same as input tensor.

Examples:
```# adaptive avg pool2d
# suppose input data in shape of [N, C, H, W], `output_size` is [m, n],
# output shape is [N, C, m, n], adaptive pool divide H and W dimensions
# of input data into m * n grids averagely and performs poolings in each
# grid to get output.
# adaptive avg pool performs calculations as follow:
#
#     for i in range(m):
#         for j in range(n):
#             hstart = floor(i * H / m)
#             hend = ceil((i + 1) * H / m)
#             wstart = floor(i * W / n)
#             wend = ceil((i + 1) * W / n)
#             output[:, :, i, j] = avg(input[:, :, hstart: hend, wstart: wend])
#