@tf_export('math.reduce_min', 'reduce_min', v1=[])
@dispatch.add_dispatch_support
def reduce_min(input_tensor, axis=None, keepdims=False, name=None):
    'Computes the minimum of elements across dimensions of a tensor.\n\n  Reduces `input_tensor` along the dimensions given in `axis`.\n  Unless `keepdims` is true, the rank of the tensor is reduced by 1 for each\n  entry in `axis`. If `keepdims` is true, the reduced dimensions\n  are retained with length 1.\n\n  If `axis` is None, all dimensions are reduced, and a\n  tensor with a single element is returned.\n\n  Args:\n    input_tensor: The tensor to reduce. Should have real numeric type.\n    axis: The dimensions to reduce. If `None` (the default), reduces all\n      dimensions. Must be in the range `[-rank(input_tensor),\n      rank(input_tensor))`.\n    keepdims: If true, retains reduced dimensions with length 1.\n    name: A name for the operation (optional).\n\n  Returns:\n    The reduced tensor.\n\n  @compatibility(numpy)\n  Equivalent to np.min\n  @end_compatibility\n  '
    keepdims = (False if (keepdims is None) else keepdims)
    return _may_reduce_to_scalar(keepdims, axis, gen_math_ops._min(input_tensor, _ReductionDims(input_tensor, axis), keepdims, name=name))
Computes the minimum of elements across dimensions of a tensor.

Reduces `input_tensor` along the dimensions given in `axis`.
Unless `keepdims` is true, the rank of the tensor is reduced by 1 for each
entry in `axis`. If `keepdims` is true, the reduced dimensions
are retained with length 1.

If `axis` is None, all dimensions are reduced, and a
tensor with a single element is returned.

Args:
  input_tensor: The tensor to reduce. Should have real numeric type.
  axis: The dimensions to reduce. If `None` (the default), reduces all
    dimensions. Must be in the range `[-rank(input_tensor),
    rank(input_tensor))`.
  keepdims: If true, retains reduced dimensions with length 1.
  name: A name for the operation (optional).

Returns:
  The reduced tensor.

@compatibility(numpy)
Equivalent to np.min
@end_compatibility
@tf_export('math.reduce_min', 'reduce_min', v1=[])
@dispatch.add_dispatch_support
def reduce_min(input_tensor, axis=None, keepdims=False, name=None):
    'Computes the minimum of elements across dimensions of a tensor.\n\n  Reduces `input_tensor` along the dimensions given in `axis`.\n  Unless `keepdims` is true, the rank of the tensor is reduced by 1 for each\n  entry in `axis`. If `keepdims` is true, the reduced dimensions\n  are retained with length 1.\n\n  If `axis` is None, all dimensions are reduced, and a\n  tensor with a single element is returned.\n\n  Args:\n    input_tensor: The tensor to reduce. Should have real numeric type.\n    axis: The dimensions to reduce. If `None` (the default), reduces all\n      dimensions. Must be in the range `[-rank(input_tensor),\n      rank(input_tensor))`.\n    keepdims: If true, retains reduced dimensions with length 1.\n    name: A name for the operation (optional).\n\n  Returns:\n    The reduced tensor.\n\n  @compatibility(numpy)\n  Equivalent to np.min\n  @end_compatibility\n  '
    keepdims = (False if (keepdims is None) else keepdims)
    return _may_reduce_to_scalar(keepdims, axis, gen_math_ops._min(input_tensor, _ReductionDims(input_tensor, axis), keepdims, name=name))