Add maximum and minimum torch wrappers (for fixed two-arg type promotion)

Author: asmeurer

array_api_compat/torch/_aliases.py

@@ -177,6 +177,8 @@ def can_cast(from_: Union[Dtype, array], to: Dtype, /) -> bool:
 logaddexp = _two_arg(torch.logaddexp)
 # logical functions are not included here because they only accept bool in the
 # spec, so type promotion is irrelevant.
+maximum = _two_arg(torch.maximum)
+minimum = _two_arg(torch.minimum)
 multiply = _two_arg(torch.multiply)
 not_equal = _two_arg(torch.not_equal)
 pow = _two_arg(torch.pow)
@@ -735,15 +737,16 @@ def sign(x: array, /) -> array:
            'atan2', 'bitwise_and', 'bitwise_left_shift', 'bitwise_or',
            'bitwise_right_shift', 'bitwise_xor', 'copysign', 'divide',
            'equal', 'floor_divide', 'greater', 'greater_equal', 'hypot',
-           'less', 'less_equal', 'logaddexp', 'multiply', 'not_equal', 'pow',
-           'remainder', 'subtract', 'max', 'min', 'clip', 'unstack',
-           'cumulative_sum', 'sort', 'prod', 'sum', 'any', 'all', 'mean',
-           'std', 'var', 'concat', 'squeeze', 'broadcast_to', 'flip', 'roll',
-           'nonzero', 'where', 'reshape', 'arange', 'eye', 'linspace', 'full',
-           'ones', 'zeros', 'empty', 'tril', 'triu', 'expand_dims', 'astype',
-           'broadcast_arrays', 'UniqueAllResult', 'UniqueCountsResult',
-           'UniqueInverseResult', 'unique_all', 'unique_counts',
-           'unique_inverse', 'unique_values', 'matmul', 'matrix_transpose',
-           'vecdot', 'tensordot', 'isdtype', 'take', 'sign']
+           'less', 'less_equal', 'logaddexp', 'maximum', 'minimum',
+           'multiply', 'not_equal', 'pow', 'remainder', 'subtract', 'max',
+           'min', 'clip', 'unstack', 'cumulative_sum', 'sort', 'prod', 'sum',
+           'any', 'all', 'mean', 'std', 'var', 'concat', 'squeeze',
+           'broadcast_to', 'flip', 'roll', 'nonzero', 'where', 'reshape',
+           'arange', 'eye', 'linspace', 'full', 'ones', 'zeros', 'empty',
+           'tril', 'triu', 'expand_dims', 'astype', 'broadcast_arrays',
+           'UniqueAllResult', 'UniqueCountsResult', 'UniqueInverseResult',
+           'unique_all', 'unique_counts', 'unique_inverse', 'unique_values',
+           'matmul', 'matrix_transpose', 'vecdot', 'tensordot', 'isdtype',
+           'take', 'sign']
 
 _all_ignore = ['torch', 'get_xp']