dlrm_data_pytorch.py [866:888]:
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):
    # dense feature
    Xt = torch.tensor(ra.rand(n, m_den).astype(np.float32))

    # sparse feature (sparse indices)
    lS_emb_offsets = []
    lS_emb_indices = []
    # for each embedding generate a list of n lookups,
    # where each lookup is composed of multiple sparse indices
    for size in ln_emb:
        lS_batch_offsets = []
        lS_batch_indices = []
        offset = 0
        for _ in range(n):
            # num of sparse indices to be used per embedding (between
            if num_indices_per_lookup_fixed:
                sparse_group_size = np.int64(num_indices_per_lookup)
            else:
                # random between [1,num_indices_per_lookup])
                r = ra.random(1)
                sparse_group_size = np.int64(
                    np.round(max([1.0], r * min(size, num_indices_per_lookup)))
                )
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dlrm_data_pytorch.py [920:942]:
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):
    # dense feature
    Xt = torch.tensor(ra.rand(n, m_den).astype(np.float32))

    # sparse feature (sparse indices)
    lS_emb_offsets = []
    lS_emb_indices = []
    # for each embedding generate a list of n lookups,
    # where each lookup is composed of multiple sparse indices
    for size in ln_emb:
        lS_batch_offsets = []
        lS_batch_indices = []
        offset = 0
        for _ in range(n):
            # num of sparse indices to be used per embedding (between
            if num_indices_per_lookup_fixed:
                sparse_group_size = np.int64(num_indices_per_lookup)
            else:
                # random between [1,num_indices_per_lookup])
                r = ra.random(1)
                sparse_group_size = np.int64(
                    np.round(max([1.0], r * min(size, num_indices_per_lookup)))
                )
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