class_docstr: The data to fit.
  
  Parameters
  ----------
  x : array_like
      Observed data for the independent variable of the regression
  y : array_like, optional
      If array-like, observed data for the dependent variable of the
      regression. A scalar input implies that the model to be used on
      the data is implicit.
  we : array_like, optional
      If `we` is a scalar, then that value is used for all data points (and
      all dimensions of the response variable).
      If `we` is a rank-1 array of length q (the dimensionality of the
      response variable), then this vector is the diagonal of the covariant
      weighting matrix for all data points.
      If `we` is a rank-1 array of length n (the number of data points), then
      the i'th element is the weight for the i'th response variable
      observation (single-dimensional only).
      If `we` is a rank-2 array of shape (q, q), then this is the full
      covariant weighting matrix broadcast to each observation.
      If `we` is a rank-2 array of shape (q, n), then `we[:,i]` is the
      diagonal of the covariant weighting matrix for the i'th observation.
      If `we` is a rank-3 array of shape (q, q, n), then `we[:,:,i]` is the
      full specification of the covariant weighting matrix for each
      observation.
      If the fit is implicit, then only a positive scalar value is used.
  wd : array_like, optional
      If `wd` is a scalar, then that value is used for all data points
      (and all dimensions of the input variable). If `wd` = 0, then the
      covariant weighting matrix for each observation is set to the identity
      matrix (so each dimension of each observation has the same weight).
      If `wd` is a rank-1 array of length m (the dimensionality of the input
      variable), then this vector is the diagonal of the covariant weighting
      matrix for all data points.
      If `wd` is a rank-1 array of length n (the number of data points), then
      the i'th element is the weight for the ith input variable observation
      (single-dimensional only).
      If `wd` is a rank-2 array of shape (m, m), then this is the full
      covariant weighting matrix broadcast to each observation.
      If `wd` is a rank-2 array of shape (m, n), then `wd[:,i]` is the
      diagonal of the covariant weighting matrix for the ith observation.
      If `wd` is a rank-3 array of shape (m, m, n), then `wd[:,:,i]` is the
      full specification of the covariant weighting matrix for each
      observation.
  fix : array_like of ints, optional
      The `fix` argument is the same as ifixx in the class ODR. It is an
      array of integers with the same shape as data.x that determines which
      input observations are treated as fixed. One can use a sequence of
      length m (the dimensionality of the input observations) to fix some
      dimensions for all observations. A value of 0 fixes the observation,
      a value > 0 makes it free.
  meta : dict, optional
      Free-form dictionary for metadata.
  
  Notes
  -----
  Each argument is attached to the member of the instance of the same name.
  The structures of `x` and `y` are described in the Model class docstring.
  If `y` is an integer, then the Data instance can only be used to fit with
  implicit models where the dimensionality of the response is equal to the
  specified value of `y`.
  
  The `we` argument weights the effect a deviation in the response variable
  has on the fit. The `wd` argument weights the effect a deviation in the
  input variable has on the fit. To handle multidimensional inputs and
  responses easily, the structure of these arguments has the n'th
  dimensional axis first. These arguments heavily use the structured
  arguments feature of ODRPACK to conveniently and flexibly support all
  options. See the ODRPACK User's Guide for a full explanation of how these
  weights are used in the algorithm. Basically, a higher value of the weight
  for a particular data point makes a deviation at that point more
  detrimental to the fit.
code: def __init__(self, x, y=None, we=None, wd=None, fix=None, meta=None):
      self.x = _conv(x)
  
      if not isinstance(self.x, numpy.ndarray):
          raise ValueError(
              "Expected an 'ndarray' of data for 'x', "
              f"but instead got data of type '{type(self.x).__name__}'"
          )
  
      self.y = _conv(y)
      self.we = _conv(we)
      self.wd = _conv(wd)
      self.fix = _conv(fix)
      self.meta = {} if meta is None else meta
  
imports: import os
  import numpy
  from warnings import warn
  from scipy.odr import __odrpack
label_desc: Use image node_id 1 to create a new Data object  with example: obj = Data(x, y, we, wd, fix, meta)