class_docstr: An array of heterogeneous-shaped, homogeneous-typed tensors.
  
  This is the Arrow side of TensorArray for tensor elements that have differing
  shapes. Note that this extension only supports non-ragged tensor elements; i.e.,
  when considering each tensor element in isolation, they must have a well-defined
  shape. This extension also only supports tensor elements that all have the same
  number of dimensions.
  
  See Arrow docs for customizing extension arrays:
  https://arrow.apache.org/docs/python/extending_types.html#custom-extension-array-class
class_docstr_tok: ["An","array","of","heterogeneous-shaped",",","homogeneous-typed","tensors",".","This","is","the","Arrow","side","of","TensorArray","for","tensor","elements","that","have","differing","shapes",".","Note","that","this","extension","only","supports","non-ragged","tensor","elements",";","i.e.",",","when","considering","each","tensor","element","in","isolation",",","they","must","have","a","well-defined","shape",".","This","extension","also","only","supports","tensor","elements","that","all","have","the","same","number","of","dimensions",".","See","Arrow","docs","for","customizing","extension","arrays",":","https",":","\/\/arrow.apache.org\/docs\/python\/extending_types.html","#","custom-extension-array-class"]
docstr: Convert the entire array of tensors into a single ndarray.
  
  Args:
      zero_copy_only: If True, an exception will be raised if the conversion to a
          NumPy array would require copying the underlying data (e.g. in presence
          of nulls, or for non-primitive types). This argument is currently
          ignored, so zero-copy isn't enforced even if this argument is true.
  
  Returns:
      A single ndarray representing the entire array of tensors.
docstr_tok: ["Convert","the","entire","array","of","tensors","into","a","single","ndarray",".","Args",":","zero_copy_only",":","If","True",",","an","exception","will","be","raised","if","the","conversion","to","a","NumPy","array","would","require","copying","the","underlying","data","(","e.g",".","in","presence","of","nulls",",","or","for","non-primitive","types",")",".","This","argument","is","currently","ignored",",","so","zero-copy","is","n't","enforced","even","if","this","argument","is","true",".","Returns",":","A","single","ndarray","representing","the","entire","array","of","tensors","."]
code: def to_numpy(self, zero_copy_only: bool = True):
      """
      Convert the entire array of tensors into a single ndarray.
  
      Args:
          zero_copy_only: If True, an exception will be raised if the conversion to a
              NumPy array would require copying the underlying data (e.g. in presence
              of nulls, or for non-primitive types). This argument is currently
              ignored, so zero-copy isn't enforced even if this argument is true.
  
      Returns:
          A single ndarray representing the entire array of tensors.
      """
      return self._to_numpy(zero_copy_only=zero_copy_only)
  
imports: import itertools
  import json
  import sys
  from typing import Iterable, Optional, Tuple, List, Sequence, Union
  from packaging.version import parse
  import numpy
  import pyarrow
  from ray.air.util.tensor_extensions.utils import _is_ndarray_variable_shaped_tensor, create_ragged_ndarray
  from ray._private.utils import _get_pyarrow_version
  from ray.util.annotations import PublicAPI
label_desc: Use image node_id 3 for calling the ArrowVariableShapedTensorArray obj's underlying member method code with example usage: obj.to_numpy(zero_copy_only) and returns: self