class_docstr: Array API compat wrapper for any numpy version
  
  NumPy < 1.22 does not expose the numpy.array_api namespace. This
  wrapper makes it possible to write code that uses the standard
  Array API while working with any version of NumPy supported by
  scikit-learn.
  
  See the `get_namespace()` public function for more details.
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docstr: Gives a new shape to an array without changing its data.
  
  The Array API specification requires shape to be a tuple.
  https://data-apis.org/array-api/latest/API_specification/generated/array_api.reshape.html
docstr_tok: ["Gives","a","new","shape","to","an","array","without","changing","its","data",".","The","Array","API","specification","requires","shape","to","be","a","tuple",".","https",":","\/\/data-apis.org\/array-api\/latest\/API_specification\/generated\/array_api.reshape.html"]
code: def reshape(self, x, shape, *, copy=None):
      """Gives a new shape to an array without changing its data.
  
      The Array API specification requires shape to be a tuple.
      https://data-apis.org/array-api/latest/API_specification/generated/array_api.reshape.html
      """
      if not isinstance(shape, tuple):
          raise TypeError(
              f"shape must be a tuple, got {shape!r} of type {type(shape)}"
          )
  
      if copy is True:
          x = x.copy()
      return numpy.reshape(x, shape)
  
imports: import math
  from functools import wraps
  import numpy
  import scipy.special
  from .._config import get_config
  from .fixes import parse_version
label_desc: Use image node_id 9 for calling the _NumPyAPIWrapper obj's underlying member method code with example usage: obj.reshape(x, shape) and returns: numpy