parameters: (a, axis=None, out=None, keepdims=False)
return_statement: return _api_internal.amin(a, axis, keepdims, out)
docstring: Return the minimum of an array or minimum along an axis.
  
      Parameters
      ----------
      a : ndarray
          Input data.
      axis : int, optional
          Axis along which to operate.  By default, flattened input is used.
      out : ndarray, optional
          Alternative output array in which to place the result.  Must
          be of the same shape and buffer length as the expected output.
          See `doc.ufuncs` (Section "Output arguments") for more details.
      keepdims : bool, optional
          If this is set to True, the axes which are reduced are left
          in the result as dimensions with size one. With this option,
          the result will broadcast correctly against the original `arr`.
  
      Returns
      -------
      min : ndarray
          Minimum of `a`. If `axis` is None, the result is an array of dimension 1.
          If `axis` is given, the result is an array of dimension
          ``a.ndim - 1``.
  
      See Also
      --------
      max :
          The maximum value of an array along a given axis, ignoring any nan.
      minimum :
          Element-wise minimum of two arrays, ignoring any nan.
  
      Notes
      -----
      NaN in the orginal `numpy` is denoted as nan and will be ignored.
  
      Don't use `min` for element-wise comparison of 2 arrays; when
      ``a.shape[0]`` is 2, ``minimum(a[0], a[1])`` is faster than
      ``min(a, axis=0)``.
  
      Examples
      --------
      >>> a = np.arange(4).reshape((2,2))
      >>> a
      array([[0., 1.],
          [2., 3.]])
      >>> np.min(a)           # Minimum of the flattened array
      array(0.)
      >>> np.min(a, axis=0)   # Minima along the first axis
      array([0., 1.])
      >>> np.min(a, axis=1)   # Minima along the second axis
      array([0., 2.])
      >>> b = np.arange(5, dtype=np.float32)
      >>> b[2] = np.nan
      >>> np.min(b)
      array(0.) # nan will be ignored
docstring_summary: Return the minimum of an array or minimum along an axis.
docstring_tokens: Return the minimum of an array or minimum along an axis .
function: def amin(a, axis=None, out=None, keepdims=False):
      """
      Return the minimum of an array or minimum along an axis.
  
      Parameters
      ----------
      a : ndarray
          Input data.
      axis : int, optional
          Axis along which to operate.  By default, flattened input is used.
      out : ndarray, optional
          Alternative output array in which to place the result.  Must
          be of the same shape and buffer length as the expected output.
          See `doc.ufuncs` (Section "Output arguments") for more details.
      keepdims : bool, optional
          If this is set to True, the axes which are reduced are left
          in the result as dimensions with size one. With this option,
          the result will broadcast correctly against the original `arr`.
  
      Returns
      -------
      min : ndarray
          Minimum of `a`. If `axis` is None, the result is an array of dimension 1.
          If `axis` is given, the result is an array of dimension
          ``a.ndim - 1``.
  
      See Also
      --------
      max :
          The maximum value of an array along a given axis, ignoring any nan.
      minimum :
          Element-wise minimum of two arrays, ignoring any nan.
  
      Notes
      -----
      NaN in the orginal `numpy` is denoted as nan and will be ignored.
  
      Don't use `min` for element-wise comparison of 2 arrays; when
      ``a.shape[0]`` is 2, ``minimum(a[0], a[1])`` is faster than
      ``min(a, axis=0)``.
  
      Examples
      --------
      >>> a = np.arange(4).reshape((2,2))
      >>> a
      array([[0., 1.],
          [2., 3.]])
      >>> np.min(a)           # Minimum of the flattened array
      array(0.)
      >>> np.min(a, axis=0)   # Minima along the first axis
      array([0., 1.])
      >>> np.min(a, axis=1)   # Minima along the second axis
      array([0., 2.])
      >>> b = np.arange(5, dtype=np.float32)
      >>> b[2] = np.nan
      >>> np.min(b)
      array(0.) # nan will be ignored
      """
      return _api_internal.amin(a, axis, keepdims, out)
function_tokens: def amin ( a , axis = None , out = None , keepdims = False ) : return _api_internal . amin ( a , axis , keepdims , out )