class_docstr: Returns the sample from numpy of the given distribution
class_docstr_tok: ["Returns","the","sample","from","numpy","of","the","given","distribution"]
docstr: Sample from NumPy.
code: def _sample_numpy(cls, dist, size, seed):
      """Sample from NumPy."""
  
      numpy_rv_map = {}
  
      sample_shape = {}
  
      dist_list = numpy_rv_map.keys()
  
      if dist.__class__.__name__ not in dist_list:
          return None
  
      import numpy
  
      if seed is None or isinstance(seed, int):
          rand_state = numpy.random.default_rng(seed=seed)
      else:
          rand_state = seed
      samp = numpy_rv_map[dist.__class__.__name__](
          dist, prod(size), rand_state
      )
      return samp.reshape(
          size + sample_shape[dist.__class__.__name__](dist)
      )
  
imports: from math import prod
  from sympy.core.basic import Basic
  from sympy.core.numbers import pi
  from sympy.core.singleton import S
  from sympy.functions.elementary.exponential import exp
  from sympy.functions.special.gamma_functions import multigamma
  from sympy.core.sympify import sympify, _sympify
  from sympy.matrices import ImmutableMatrix, Inverse, Trace, Determinant, MatrixSymbol, MatrixBase, Transpose, MatrixSet, matrix2numpy
  from sympy.stats.rv import _value_check, RandomMatrixSymbol, NamedArgsMixin, PSpace, _symbol_converter, MatrixDomain, Distribution
  from sympy.external import import_module
label_desc: Use image node_id 2 for calling the SampleMatrixNumpy obj's underlying member method code with example usage: obj._sample_numpy(cls, dist, size, seed) and returns: samp, None