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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:

def _crop_pad_default(x, size, padding_mode='reflection', row_pct:uniform = 0.5, col_pct:uniform = 0.5):
    "Crop and pad tfm - `row_pct`,`col_pct` sets focal point."
    padding_mode = _pad_mode_convert[padding_mode]
    size = tis2hw(size)
    if x.shape[1:] == torch.Size(size): return x
    rows,cols = size
    row_pct,col_pct = _minus_epsilon(row_pct,col_pct)
    if x.size(1)<rows or x.size(2)<cols:
        row_pad = max((rows-x.size(1)+1)//2, 0)
        col_pad = max((cols-x.size(2)+1)//2, 0)
        x = F.pad(x[None], (col_pad,col_pad,row_pad,row_pad), mode=padding_mode)[0]
    row = int((x.size(1)-rows+1)*row_pct)
    col = int((x.size(2)-cols+1)*col_pct)
    x = x[:, row:row+rows, col:col+cols]
    return x.contiguous()