def subsample(partitions,dataset,seed):
parts=np.arange(dataset.shape[<NUM_LIT:0>]/partitions,dataset.shape[<NUM_LIT:0>],dataset.shape[<NUM_LIT:0>]/partitions).astype(int)    <EOL>subOut={}<EOL>for i in range(parts.shape[<NUM_LIT:0>]):<EOL><INDENT>subOut["<STR_LIT>".format(parts[i])]=np.asarray(shuffle(dataset,random_state=seed))[<NUM_LIT:0>:parts[i],:]<EOL><DEDENT>return subOut<EOL>
Function to generate randomly sampled datasets with replacement. This is in the context of cells in the native dataset
which are the rows of the matrix
:param partitions: int designating the number of evenly spaced sample sizes to randomly select from the native dataset
:param dataset: DataFrame of the native dataset compatible with the suffle function
:param seed: pseudorandom seed, compatible with the replicate wrapper since it adds the index to the seed
:return subOut: dictionary of the randomly sampled datasets, keys are the number of cells