parameters: (dense_shape, values, inner_indices, outer_indices, ort_device)
return_statement: return SparseTensor(C.SparseTensor.sparse_csr_from_numpy(dense_shape, values, inner_indices, outer_indices,
                              ort_device._get_c_device()))
docstring: Factory method to construct a SparseTensor in CSR format from given arguments
  
          :param dense_shape: 1-D numpy array(int64) or a python list that contains a dense_shape of the
              sparse tensor (rows, cols) must be on cpu memory
          :param values: a  contiguous, homogeneous 1-D numpy array that contains non-zero elements of the tensor
              of a type.
          :param inner_indices:  contiguous 1-D numpy array(int64) that contains CSR inner indices for the tensor.
              Its length must be equal to that of the values.
          :param outer_indices:  contiguous 1-D numpy array(int64) that contains CSR outer indices for the tensor.
              Its length must be equal to the number of rows + 1.
          :param ort_device: - describes the backing memory owned by the supplied nummpy arrays. Only CPU memory is
              suppored for non-numeric data types.
  
          For primitive types, the method will map values and indices arrays into native memory and will use them as
          backing storage. It will increment the reference count and it will decrement then count when it is GCed.
          The buffers may reside in any storage either CPU or GPU.
          For strings and objects, it will create a copy of the arrays in CPU memory as ORT does not support those
          on other devices and their memory can not be mapped.
docstring_summary: Factory method to construct a SparseTensor in CSR format from given arguments
docstring_tokens: Factory method to construct a SparseTensor in CSR format from given arguments
function: def sparse_csr_from_numpy(dense_shape, values, inner_indices, outer_indices, ort_device):
          '''
          Factory method to construct a SparseTensor in CSR format from given arguments
  
          :param dense_shape: 1-D numpy array(int64) or a python list that contains a dense_shape of the
              sparse tensor (rows, cols) must be on cpu memory
          :param values: a  contiguous, homogeneous 1-D numpy array that contains non-zero elements of the tensor
              of a type.
          :param inner_indices:  contiguous 1-D numpy array(int64) that contains CSR inner indices for the tensor.
              Its length must be equal to that of the values.
          :param outer_indices:  contiguous 1-D numpy array(int64) that contains CSR outer indices for the tensor.
              Its length must be equal to the number of rows + 1.
          :param ort_device: - describes the backing memory owned by the supplied nummpy arrays. Only CPU memory is
              suppored for non-numeric data types.
  
          For primitive types, the method will map values and indices arrays into native memory and will use them as
          backing storage. It will increment the reference count and it will decrement then count when it is GCed.
          The buffers may reside in any storage either CPU or GPU.
          For strings and objects, it will create a copy of the arrays in CPU memory as ORT does not support those
          on other devices and their memory can not be mapped.
          '''
          return SparseTensor(C.SparseTensor.sparse_csr_from_numpy(dense_shape, values, inner_indices, outer_indices,
                              ort_device._get_c_device()))
function_tokens: def sparse_csr_from_numpy ( dense_shape , values , inner_indices , outer_indices , ort_device ) : return SparseTensor ( C . SparseTensor . sparse_csr_from_numpy ( dense_shape , values , inner_indices , outer_indices , ort_device . _get_c_device ( ) ) )